ENASE 2025 Abstracts

Area 1 - Challenges and Novel Approaches to Systems and Software Engineering (SSE)
Full Papers Short Papers
Area 2 - Systems and Software Engineering (SSE) for Emerging Domains
Full Papers Short Papers
Area 3 - Systems and Software Quality
Full Papers Short Papers
Area 4 - Theory and Practice of Systems and Applications Development
Full Papers Short Papers

Area 1 - Challenges and Novel Approaches to Systems and Software Engineering (SSE)

Full Papers
Paper Nr: 76
Title:

Evaluating ChatGPT's Ability to Detect Naming Bugs in Java Methods
Authors:

Kinari Nishiura, Atsuya Matsutomo and Akito Monden
Abstract: In Java programming, large-scale and complex functions are realized by combining multiple methods. When method names do not match their functionality, readability decreases, making maintenance challenging. Although several machine learning models have been proposed to detect such naming bugs, they require extensive training data, limiting user accessibility. Recently, large language models (LLMs) like ChatGPT have gained popularity and show potential for code comprehension tasks. This study evaluates the performance of ChatGPT in detecting naming bugs using the same datasets as in previous machine learning studies. We evaluated detection accuracy through traditional methods, various prompt adjustments, and more direct approaches. The results indicate that, while ChatGPT does not surpass traditional models, it can match their accuracy with appropriately structured prompts, requiring no additional training.
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Paper Nr: 91
Title:

Reshaping Reality: Creating Multi-Model Data and Queries from Real-World Inputs
Authors:

Irena Holubová, Alžběta Šrůtková and Jáchym Bártík
Abstract: The variety characteristic of Big Data introduces significant challenges for verified single-model data management solutions. The central issue lies in managing the multi-model data. As more solutions appear, especially in the database world, the need to benchmark and compare them rises. Unfortunately, there is a lack of available real-world multi-model datasets, the number of multi-model benchmarks is still small, and their general usability is limited. This paper proposes a solution that enables creation of multi-model data from virtually any given single-model dataset. We introduce a framework that enables automatic inference of the schema of input data, its user-defined modification and mapping to multiple models, and the data generation reflecting the changes. Using the well-known Yelp dataset, we show its advantages and usability in three scenarios reflecting reality.
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Paper Nr: 140
Title:

Maude Strategies-Based SoSs Workflow Modeling
Authors:

Charaf Eddine Dridi, Nabil Hameurlain and Faiza Belala
Abstract: Systems of Systems (SoSs) are large-scale, complex, and occasionally critical software systems. They emerge from the integration of autonomous, heterogeneous, and evolving Constituent Systems (CSs) that collaborate to fulfill operational missions and provide functionalities that exceed those of individual systems. The modeling, simulation, and analysis of SoSs are challenging due to complexities such as temporal constraints on missions and the emergence of both desired and unwanted behaviors within CSs. In this paper, we propose a formal-based solution to specify and validate the functional behavior of SoSs. We employ the Maude Strategy language, a rewriting logic-based language to define the operational semantics of these systems. This includes implementing a set of strategies for managing mission execution, which aim to enhance the SoS workflow by avoiding undesirable behaviors and promoting desirable ones. Our approach offers an executable solution for these strategies and validates the SoS behavior using model-checking techniques provided by Maude. To demonstrate its applicability, the approach is illustrated with a case study of a French Emergency SoS.
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Short Papers
Paper Nr: 14
Title:

Optimising IIoT Control Systems at Demcon: Integrating MQTT, Sparkplug B, and ISA-88 for Unified Automation
Authors:

Ana Pintilie, Remco Poelarends and Andrea Capiluppi
Abstract: This paper addresses the challenges in optimizing PLC-based industrial control systems at Demcon to meet IIoT standards. Through a collaboration with the University of Groningen (NL), we redesigned the architecture using MQTT and Sparkplug B to enable scalable, real-time communication and introduced a Unified Namespace (UNS) for seamless data exchange. The results demonstrate improved flexibility, scalability, and latency reduction, validating the approach in an industrial environment and highlighting its broader potential for IIoT adoption.
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Paper Nr: 43
Title:

Genetic Algorithm for Optimal Response Time Scheduling of Electric Vehicle Model
Authors:

Zouhaira Abdellaoui and Houda Meddeb
Abstract: Genetic Algorithms (GAs) are widely recognized for their ability to solve complex optimization problems. Gas are an effective computational tool designed to identify optimal solutions for optimization issues in electrical vehicle. In this context, we have developed GA for optimizing the response time based on static scheduling suspension model of SAE Benchmark electric vehicles. The implemented architecture consists of multiple nodes connected via the Real- Time middleware Data Distribution Service (DDS) and the protocol FlexRay in order to benefit from their high speed and QoS.
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Paper Nr: 44
Title:

Lessons Learned from Implementing a Language-Agnostic Dependency Graph Parser
Authors:

Francesco Refolli, Darius Sas and Francesca Arcelli Fontana
Abstract: In software engineering, automated tools are essential for detecting policy violations within code. These tools typically analyze the relationships and dependencies between components in large codebases, which may be written in various programming languages. Most available tools, whether free or proprietary, rely on third-party software to perform statistical analyses. This approach often requires a separate tool for each programming language, which can lead to high maintenance efforts, and even relying on a standardized technology such as Language Servers has several drawbacks. This paper investigates the feasibility of removing language-specific dependencies in the construction of dependency graphs by using two libraries: Tree Sitter and Stack Graph. After analyzing the capabilities of these technologies, their application in this context is demonstrated, and the effectiveness and accuracy of the proposed solution are evaluated.
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Paper Nr: 119
Title:

Agile Retrospectives: What Went Well? What Didn't Go Well? What Should We Do?
Authors:

Maria Spichkova, Hina Lee, Kevin Iwan, Madeleine Zwart, Yuwon Yoon and Xiaohan Qin
Abstract: In Agile/Scrum software development, the idea of retrospective meetings (retros) is one of the core elements of the project process. In this paper, we present our work in progress focusing on two aspects: analysis of potential usage of generative AI for information interaction within retrospective meetings, and visualisation of retros' information to software development teams. We also present our prototype tool RetroAI++, focusing on retros-related functionalities.
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Paper Nr: 127
Title:

Towards a Cultural Perspective on Human-Robot Interaction
Authors:

Bhavishya Swami, Jeshwitha Jesus Raja, Meenakshi Manjunath, Amruta Rout and Marian Daun
Abstract: Human–robot interaction is a rapidly emerging field whose scope and definition remain diffuse due to its broad application across diverse robotics domains. Research in human–robot interaction typically moves beyond simple input–output interfaces to explore more complex interactions, such as physical collaboration between humans and robots. Consequently, various perspectives on human–robot interaction—ranging from technological considerations and cooperation modalities to trust and safety—have proliferated in both research and practice. Although the priorities in human–robot interaction research often reflect industry demands and societal values, the cultural context in which these priorities evolve has received limited attention. In particular, how different countries’ expectations shape the perceived importance of human–robot interaction perspectives remains under-explored. A deeper understanding of these cross-cultural differences can foster a global view of human–robot interaction and support the transfer of best practices across borders. Therefore, this paper examines representative case studies from Germany and India, highlighting key divergences in how human–robot interaction is defined and approached in different cultural and industrial contexts.
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Paper Nr: 129
Title:

Students' Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications
Authors:

Ioana-Georgiana Ciuciu and Manuela-Andreea Petrescu
Abstract: The study investigates students’ interest and expectations in a Big Data Engineering course integrated with a Master curricula, as well as ethical implications of using Big Data. An anonymous online survey was conducted with 42 of the 67 students enrolled in the Big Data course offered to Computer Science and Bioinformatics Master’s programs. The responses were analyzed and interpreted using thematic analysis, highlighting interesting aspects related to students’ expectations, interest, and their perspective of the ethical implications of working with Big Data. The study concludes that, even though there is significant difference in students’ background, the majority are interested in learning Big Data, for practical and personal reasons related to the potential for career growth and their passion for the field. The main expectation expressed is related to enhancing their knowledge related to Big Data via practical activities. All students demonstrate awareness of potential ethical threats related to security and privacy, while Computer Science students are aware of the possibility of introducing bias in data during acquisition and analysis and of potential abusive data usage.
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Paper Nr: 135
Title:

Security Engineering in Cyber-Physical Systems: A Systematic Review of Methodological Approaches
Authors:

Elias Seid, Oliver Popov and Fredrik Blix
Abstract: Ensuring strong security in Cyber-Physical Systems (CPS) is increasingly essential as these systems become integral to contemporary industrial and societal infrastructures. The increasing prevalence of security risks requires the advancement of conventional security engineering approaches to tackle the distinct problems presented by CPS. This study offers a thorough assessment of the research methodologies, approaches, and strategies used in security engineering for cyber-physical systems over the last fifteen years. The review analyses the design and execution of security solutions, including empirical and conceptual investigations, along with the integration and enhancement of existing methodologies. This study seeks to offer a systematic overview of contemporary developments and pinpoint methodological concerns essential for future research in adaptive and security engineering -driven for CPS through an analysis of diverse literature. This study enhances the current discussion by providing a thorough analysis of the research environment, demonstrating the requirement for new and contextually relevant security engineering methodologies.
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Paper Nr: 23
Title:

Querying Digital Twin Models
Authors:

Emilio Carrión, Pedro Valderas and Óscar Pastor
Abstract: Digital twins (DTs) have become increasingly complex as they integrate data from diverse heterogeneous sources while requiring strict security controls. This heterogeneity presents challenges to effectively query and aggregate information. The Entity-Relationship Digital Twin (ERDT) model provides a basis for representing both physical entities and their digital counterparts, however it lacks mechanisms to handle system-level queries and ensure secure access. This work extends the ERDT model by introducing query views, high-level abstractions that allows flexible and secure querying of DT data over multiple entities. Furthermore, we validate our approach through a real-world industrial case study within Mercadona’s logistics operations where we specifically focus on their truck fleet management system. The results demonstrate that our solution covers data heterogeneity and security constraints while also providing enhanced query capabilities in a production environment.
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Paper Nr: 42
Title:

Trends and Challenges in Machine Learning for Code Summarization and Comprehension: A Systematic Literature Review
Authors:

Panagiotis Mantos, Fotios Kokkoras and George Kakarontzas
Abstract: This systematic literature review explores current trends in automatic source code summarization and comprehension. Through extraction and analysis of information from six reputable digital libraries, we answered the following three questions: a) Which are the current machine learning models to generate summaries for source code? b) What factors should be considered when selecting an appropriate machine learning model for code summarization and comprehension? c) What are the possible future directions for research and development in machine learning for code summarization and comprehension, considering current limitations and emerging trends. The findings show significant progress with deep learning methods dominating this area.
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Paper Nr: 48
Title:

An Innovative Approach to Represent Tacit Knowledge of Fishing with Knowledge Graphs
Authors:

Tanjila Kanij, Shafia Husna, Afzal Azeem Chowdhary, Misita Anwar, Md. Khalid Hossain and John Grundy
Abstract: Fisherfolk communities in developing nations face marginalization due to low literacy levels and socio-economic challenges, leading to reduced interest in fishing and the loss of vital, undocumented tacit knowledge. To address this, we conducted focus group discussions and interviews in Bangladesh and Indonesia, extracting knowledge components from the conversational data to develop knowledge graphs. These graphs visually represent facts, attributes, and relationships, facilitating knowledge extraction. We compared manual and automated graph development using Large Language Models (LLMs), demonstrating their potential to systematically identify, preserve, and share critical fishing knowledge. Future work involves testing this framework with fisherfolk to preserve and disseminate this essential knowledge.
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Paper Nr: 80
Title:

Interpreting Workflow Architectures by LLMs
Authors:

Michal Töpfer, Tomáš Bureš, František Plášil and Petr Hnětynka
Abstract: In this paper, we focus on how reliably can a Large Lanuage Model (LLM) interpret a software architecture, namely a workflow architecture (WA). Even though our initial experiments show that an LLM can answer specific questions about a WA, it is unclear how correct its answers are. To this end, we propose a methodology to assess whether an LLM can correctly interpret a WA specification. Based on the conjecture that the LLM needs to correctly answer low-abstraction level questions to answer questions at a higher abstraction level properly, we define a set of test patterns, each of them providing a template for low-abstraction level questions, together with a metric for evaluating the correctness of LLM’s answers. We posit that having this metric will allow us not only to establish which LLM model works the best with WAs, but also to determine what their concrete syntax and concepts are suitable to strengthen the correctness of LLM’s interpretability of WA specifications. We demonstrate the methodology on the workflow specification language developed for a currently running Horizon Europe project.
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Paper Nr: 99
Title:

Anomaly Detection in Surveillance Videos
Authors:

Priyanka H, Ankitha A C, Pratyusha Satish Rao, Urja Modi and Chandu Naik
Abstract: This paper presents a novel approach to anomaly detection in surveillance videos, focusing specifically on accident detection. Our proposed system integrates YOLOv8 and Convolutional Neural Networks (CNN) to create a hybrid model that efficiently detects accidents in real-time and generates alerts to the nearest police station. The YOLOv8 framework is employed for object detection, ensuring high accuracy and speed, while the CNN enhances the classification of detected anomalies. Additionally, we have implemented a vehicle license plate recognition system using YOLOv8 in conjunction with PaddleOCR for character detection, enabling the extraction of vehicle information during incidents. The results demonstrate the effectiveness of our approach in improving response times and enhancing public safety through automated alert generation and vehicle identification. This research contributes to the ongoing efforts in leveraging advanced machine learning techniques for real-world applications in surveillance and public safety.
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Paper Nr: 110
Title:

On Improving the Efficiency of AI-Generated Text Detection
Authors:

Bogdan Ichim and Andrei-Cristian Năstase
Abstract: This paper proposes methods of making AI-Generated Text Detectors more computationally efficient without paying a high price in prediction accuracy. Most AI-Detectors use transformer-based architectures with high-dimensional text embedding vectors involved in the pipelines. Applying dimension reduction algorithms to these vectors is a simple idea for making the whole process more efficient. Our experimental results reveal that this may lead from 5 up to 500 times improvements in the training and inference times, with only marginal performance degradation. These findings suggest that integrating such methods in largescale systems could be an excellent way to enhance the processing speed (and also reduce the electric energy consumption). In particular, real-time applications might benefit from such enhancements.
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Area 2 - Systems and Software Engineering (SSE) for Emerging Domains

Full Papers
Paper Nr: 21
Title:

A Conceptual Model-Based Application for the Treatment and Management of Data in Pediatric Oncology: The Neuroblastoma Use Case
Authors:

Jesús Carreño-Bolufer, José Fabián Reyes Román, Sergio Pérez Andrés, Désirée Ramal Pons, Víctor Juárez Vidal, Adela Cañete Nieto and Óscar Pastor
Abstract: Neuroblastoma is one of the leading causes of death in childhood oncology. Current treatments for these patients are general and not targeted, including radiotherapy, chemotherapy, and surgery. There is a need for more efficient methods. Precision Medicine (PM) can help to overcome this challenge. PM incorporates clinical, lifestyle, and genomic data, among others, into a standardized process to provide individualized treatment. However, a large amount of data is needed to achieve PM, and the heterogeneity present in the case of neuroblastoma poses a challenge for integration and, consequently, for knowledge generation. We need a solid domain definition that provides a foundation for experts to work on, which implies generating a conceptual model. Based on this model, any Information System (IS) can be developed. ISs play a vital role in managing clinical data efficiently. Much of the clinical data has been captured and managed over the years with inefficient tools such as spreadsheets. In this work, we first present the new Conceptual Model of Neuroblastoma (CMN), with a special focus on genomics, and second, ClinGenNBL, a conceptual model-based web application that implement the CMN with the goal of assisting clinicians in managing patients with neuroblastoma through a user-friendly interface.
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Paper Nr: 34
Title:

Enhancing Privacy, Censorship Resistance, and User Engagement in a Blockchain-Based Social Network
Authors:

Myo Thiha, Halil Yetgin, Luca Piras and Mohammed Ghazi Al-Obeidallah
Abstract: In the contemporary digital era, online social networks have become integral to global communication, facilitating connectivity and information dissemination. Millions of people use centralised social media platforms today, which raises concerns about user control, privacy, and censorship. These platforms profit from user data and content, as the single authority of these platforms has complete control over user data. Although peer-to-peer decentralised online social networks were developed to address the weaknesses in centralised platforms, they still have significant limitations in terms of securing privacy, and handling censorship resistance issues. In this work, we propose a novel decentralised online social network leveraging blockchain technology to address these pressing issues in centralised and peer-to-peer online social networks. The proposed system prioritizes user control by decentralizing data storage and network governance, thereby reducing the issues associated with centralized control. By employing blockchain technology, individuals maintain ownership of their data and gain greater control, thereby enhancing user privacy protection. Additionally, the cryptographic security and immutable ledger of blockchain technology protect freedom of expression and information exchange by resisting censorship. Moreover, with the integration of incentivization mechanisms, users are incentivized to contribute to the network’s growth and sustainability, as well as promoting engaging content and encouraging ownership among users. The evaluation results show that our blockchain-based decentralised online social network (DOSN) accomplishes the aim and objectives for preserving privacy, censorship resistance and enhancing user engagement in online social network with the use of blockchain technology.
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Paper Nr: 67
Title:

uBSaaS: A Unified Blockchain Service as a Service Framework for Streamlined Blockchain Services Integration
Authors:

Huynh Thanh Thien Pham, Frank Jiang, Lei Pan, Alessio Bonti and Mohamed Abdelrazek
Abstract: Blockchain application development remains complex and costly due to specialized cryptographic requirements and platform-specific protocols. Existing solutions often provide only isolated services, hindering cross-chain interoperability and limiting broader adoption. This paper addresses these gaps by introducing SChare, a platform founded on a unified Blockchain Service as a Service (uBSaaS) framework that abstracts blockchain-intensive tasks into microservices. This architecture enables developers to integrate blockchain features into applications as seamlessly as any third-party service, while supporting orchestration across multiple blockchain networks for enhanced flexibility. We evaluate the platform through an experimental cross-chain application to demonstrate feasibility and scalability. Additionally, a developer study involving hands-on usage and post-study assessments highlights SChare’s effectiveness in reducing both the steep learning curve and overall development overhead. The results indicate that SChare facilitates more accessible blockchain development, thereby encouraging wider adoption. This approach advances the state of the art by unifying platform-specific capabilities, fostering interoperability, and offering a scalable, microservices-based solution for blockchain application development.
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Paper Nr: 111
Title:

VRSLOG: An Approach to Log Immersive Experiences in Virtual Reality Systems
Authors:

Divij D., Y. Raghu Reddy, Radha Krishna B. and Sai Anirudh Karre
Abstract: Software developers commonly use logging mechanisms to gather runtime data. Over the years, this information has been used for various purposes like debugging, behavioral analysis, system comprehension, etc. Frameworks such as log4j and Logback have helped standardize logging practices by incorporating Software Engineering principles. Logging interactions in Virtual Reality (VR) applications can help understand user behavior and assist with automated conformance checks. In this paper, we introduce VRSLOG, a generalized logging framework that can capture interactions across diverse VR scenes without any changes to the framework. We implement a prototype of the VRSLOG framework and demonstrate the generation of log files. Further, the log files are used to conduct conformance checks against a predefined expected sequence of events within the VR scene.
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Short Papers
Paper Nr: 19
Title:

Runtime Verification for Deep Learning Systems
Authors:

Birk Torpmann-Hagen, Michael A. Riegler, Pål Halvorsen and Dag Johansen
Abstract: Deep Neural Networks are being utilized in increasingly numerous software systems and across a wide range of data modalities. While this affords many opportunities, recent work has also shown that deep learning systems often fail to perform up to specification in deployment scenarios, despite initial tests often indicating excellent results. This disparity can be attributed to shifts in the nature of the input data at deployment time and the infeasibility of generating test cases that sufficiently represent data that have undergone such shifts. To address this, we leverage recent advances in uncertainty quantification for deep neural networks and outline a framework for developing runtime verification support for deep learning systems. This increases the resilience of the system in deployment conditions and provides an increased degree of transparency with respect to the system’s overall real-world performance. As part of our framework, we review and systematize disparate work on quantitative methods of detecting and characterizing various failure modes in deep learning systems, which we in turn consolidate into a comprehensive framework for the implementation of flexible runtime monitors. Our framework is based on requirements analysis, and includes support for multimedia systems and online learning. As the methods we review have already been empirically verified in their respective works, we illustrate the potential of our framework through a proof-of-concept multimedia diagnostic support system architecture that utilizes our framework. Finally, we suggest directions for future research into more advanced instrumentation methods and various framework extensions. Overall, we envision that runtime verification may endow multimedia deep learning systems with the necessary resilience required for deployment in real-world applications.
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Paper Nr: 88
Title:

Sustainable Software Development: An ADKAR-Based Framework for Project Managers and Teams
Authors:

Yassine Talas and Hajer Rabii
Abstract: This ongoing preliminary research addresses the growing need for environmentally conscious practices in Information Technology (IT), specifically in software development. It aims to develop a generic framework for sustainable software development (SSD) tailored to IT project managers and teams, through leveraging the ADKAR change management model and its five pillars (Awareness, Desire, Knowledge, Ability, Reinforcement). This work combines two complementary research methodologies: interviews and participatory action research. The current findings include the overall structure of the framework and suggest an alignment of the proposed framework with the Agile project management methodology. Further research is under progress to develop the detailed content of the framework, and test it. The main contribution expected from this work is to promote the democratization of sustainable practices in software development.
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Paper Nr: 108
Title:

VReqDV: Model Based Design Generation & Design Versioning Tool for Virtual Reality Product Development
Authors:

Shambhavi Jahagirdar, Sai Anirudh Karre and Y. Raghu Reddy
Abstract: Virtual Reality (VR) product requires expertise from diverse set of stakeholders. Moving from requirements to design mock-up(s) while building a VR product is an iterative process and requires manual effort. Due to lack of tool support, creating design templates and managing the respective versions turns out to be laborious and difficult. In this paper, we describe VReqDV, a model-driven VR design generation and versioning tool that can address this gap. The tool uses VR meta-model template as a foundation to facilitate a design pipeline. VReqDV can potentially facilitate design generation, design viewing, design versioning, design to requirements conformity, traceability, and maintenance. It is a step forward in creating a Model-Driven Development pipeline for VR scene design generation. We demonstrate the capabilities of VReqDV using a simple game scene and share our insights for wider adoption by the VR community.
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Paper Nr: 124
Title:

Integrating Data Lakes with Self-Adaptive Serious Games
Authors:

Michalis Pingos, Spyros Loizou and Andreas S. Andreou
Abstract: Big Data challenges traditional data management tools due to their size, speed of generation, and variety of formats. The application of Big Data has become essential in areas like serious games, where it enhances functionality and effectiveness. Serious games benefit significantly from Big Data analytics, allowing for real-time data collection, processing and analysis of a vast number of users/players and their interactions. Traditional data management systems strive to handle the complexity of Big Data, particularly in environments like serious games, where diverse data sources create heterogeneity. This paper presents an approach that employs Data Lakes and semantic annotation as a solution for providing a scalable, flexible storage system for raw and unstructured data, enabling real-time processing and efficient management of Big Data produced by serious games. The effectiveness of the proposed approach is demonstrated through a speech therapy game example developed for the purposes of this study. A qualitative evaluation and comparison with two rival approaches is also performed using a set of criteria introduced in this work. The proposed approach offers an effective solution for handling data in multi-user gaming environments thus enhancing adaptability, personalization, and functional flexibility of serious games, and driving better user engagement and outcomes.
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Paper Nr: 138
Title:

Generating Formal Process Models and Decisions from Examination Rules in Natural Text with ChatGPT
Authors:

Andreas Speck, Melanie Windrich, Jan Hesse, Melina Sentz, David Kuhlen, Thomas Stuht and Elke Pulvermüller
Abstract: Many business and administrative systems are modeled with business process models in a notation like Business Process Model and Notation (BPMN) supported by tools like Camunda (BPMN, 2025). When such systems are to be build in most cases the requirements and rules are recorded in plain text. This raises the desire using AI tools (artificial intelligence) for generating business process models from the text. The question is to which extend AI techniques may support the development of formal process models. We apply ChatGPT to analyze judicial regulations written natural text (examination regulation) and request transforming the text to process models and decision diagrams as an XML exchange file which may be displayed in the Camunda Modeler.
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Paper Nr: 98
Title:

Machine Learning for Ontology Alignment
Authors:

Faten Abbassi, Yousra Bendaly Hlaoui and Faouzi Ben Charrada
Abstract: This article proposes an ontology alignment approach that combines supervised machine learning models and schema-matching techniques. Our approach analyzes reference ontologies and their alignments provided by OAEI to extract ontological data matrices and confidence vectors. In addition, these ontological data matrices are normalized using normalization techniques to obtain a coherent format for enhancing the accuracy of the alignments. From the normalized data, syntactic and external similarity matrices are generated via individual matchers before being concatenated to build a final similarity matrix representing the correspondences between two ontologies. This matrix and the confidence vector are then used by six machine learning models, such as Logistic Regression, Random Forest Classifier, Neural Network, Linear SVC, K-Neighbors Classifier and Gradient Boosting Classifier, to identify ontological similarities. To evaluate the performances of our approach, we have compared our results with our previous results (Abbassi and Hlaoui, 2024a). The experiments are performed over the reference ontologies of the benchmark and conference tracks based on their reference alignments provided by OAEI.
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Paper Nr: 109
Title:

Integrating Process Mining and Success Factors in Information Systems Projects: A Decision Support System Approach
Authors:

Joana Pedrosa, Luís Gonzaga Magalhães and Ricardo Martinho
Abstract: The management of Information Systems (IS) projects involves addressing complex challenges such as communication issues, resource allocation, time constraints, customer interaction and evolving requirements. A project manager faces, therefore, a significant number of decisions on the progress of these projects, based on the most important Success Factors (SF) that each project encloses. As far as we are aware, there is currently no automated solution capable of effectively tackling these challenges, forcing managers to depend on conventional approaches that often prove insufficient to provide the necessary support. This paper proposes an architecture for a Decision Support System (DSS) designed to enhance project success by providing project managers with recommendations. The DSS integrates Process Mining techniques with SF to suggest valuable insights for decision-making. The system proposed aims to optimize project decisional outcomes and can combine algorithms from Process Mining, Data Mining, and Predictive Mining to enhance its recommendations.
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Area 3 - Systems and Software Quality

Full Papers
Paper Nr: 81
Title:

The Scent of Test Effectiveness: Can Scriptless Testing Reveal Code Smells?
Authors:

Olivia Rodríguez-Valdés, Domenico Amalfitano, Otto Sybrandi, Beatriz Marín and Tanja Vos
Abstract: This paper presents an industrial experience applying random scriptless GUI testing to the Yoho web application developed by Marviq. The study was motivated by several key challenges faced by the company, including the need to optimise testing resources, explore how random testing can complement manual testing, and investigate new coverage metrics, such as “code smell coverage”, to assess software quality and maintainability. We conducted an experiment to explore the impact of the number and length of random GUI test sequences on traditional adequacy metrics, the complementarity of random with manual testing, and the relationship between code smell coverage and traditional code coverage. Using Testar for scriptless testing and SonarQube code smell identification, results show that longer random test sequences yielded better test adequacy metrics and increased code smell coverage. In addition, random testing offers promising efficiency in test coverage and detects unique smells that manual testing might overlook. Additionally, including code smell coverage provides valuable insights into long-term code maintainability, revealing gaps that traditional metrics may not capture. These findings highlight the benefits of combining functional testing with metrics assessing code quality, particularly in resource-constrained environments.
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Paper Nr: 139
Title:

Cross-Version Defect Prediction: Does Excessive Train-Test Similarity Affect the Reliability of Evaluation?
Authors:

Zsuzsanna Oneț-Marian and Diana-Lucia Hotea
Abstract: Software Defect Prediction is defined as the automated identification of defective components within a software system. Its significance and applicability are extensive. The most realistic way of performing defect prediction is in the cross-version scenario. However, although emerging, this scenario is still relatively understudied. The prevalent approach in the cross-version defect prediction literature is to consider two successive software versions as the train-test pair, expecting them to be similar to each other. Some approaches even propose to increase this similarity by augmenting or, on the contrary, filtering the training set derived from historical data. In this paper, we analyze in detail the similarity between the instances in 28 pairs of successive software versions and perform a comparative supervised machine learning study to assess its impact on the reliability of cross-version defect prediction evaluation. We employ three ensemble learning models, Random Forest, AdaBoost and XGBoost, and evaluate them in different scenarios. The experimental results indicate that the soundness of the evaluation is questionable, since excessive train-test similarity, in terms of identical or highly similar instances, inflates the measured performance.
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Short Papers
Paper Nr: 22
Title:

Mutation Operators for Mutation Testing of Angular Web Applications
Authors:

Sarah Augustin, Hendrik Winkelmann and Herbert Kuchen
Abstract: Mutation testing is an approach for assessing the quality of a test suite by using mutation operators to insert changes into the code and then checking whether the test suite can detect the inserted changes. Due to the growing prevalence and complexity of web applications, the importance of web testing has increased, making mutation testing a potentially beneficial approach for web applications. Since in web applications, mostly web-specific mistakes and not generic mistakes occur, the question arises, to whether new mutation operators simulating such realistic, web-specific mistakes perform better than the traditional, generic mutation operators. The work at hand addresses this question by developing new mutation operators specific to the client-side TypeScript code of Angular web applications and evaluating how they perform in comparison to the traditional mutation operators. The findings indicate that the new web-specific mutation operators introduce fewer, more realistic, and harder-to-kill mutants than the traditional mutation operators, thus being a promising approach for assessing the test suite quality of web applications.
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Paper Nr: 35
Title:

Challenges in Software Metrics Adoption: Insights from Cluj-Napoca's Development Community
Authors:

Laura Diana Cernău, Laura Dioșan and Camelia Șerban
Abstract: Established research directions yield concrete outcomes on the benefits of using software metrics in software development processes, such as notable correlations between software metric values and various quality attributes of software systems or defect prediction. A discrepancy exists between academic proposals and actual practices used in software development, influenced by factors like budget constraints, prioritisation, and misconceptions regarding software metrics’ purpose and potential applications. Consequently, this study seeks to document current practices concerning the usage of software metrics, as well as the advantages and challenges associated with their integration into the software development process. This questionnaire is based on a survey of 40 participants occupying various roles in software systems development teams based in Cluj-Napoca, Romania. Most subjects mentioned that improving confidence in metric usage involves better prioritisation and understanding of metric interpretation. On the other hand, the main reasons for participants not using software metrics are lack of awareness and proper prioritisation. Although this study has revealed the existence of various software metrics-related concepts within industry software development processes, it is apparent that their capabilities are not fully understood.
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Paper Nr: 45
Title:

A Study on Different Spectra in Fault Localization
Authors:

Nícolas Hamparsomian and Marcos Lordello Chaim
Abstract: We present an experimental study to assess the impact of different spectra in fault localization. We evaluated one machine learning-based technique (Deep Neural Networks—DNN) and two Spectrum-based fault localization (Ochiai and Tarantula). These techniques were applied on 319 faulty versions of industry-like programs with real bugs using control (statements) and data (definition use associations—DUA) flow coverage as spectra. The results suggest that DNN does not benefit from data flow spectra and any spectrum will generate similar results using either Ochiai or Tarantula. Among the techniques and spectra assessed, Ochiai using control flow seems to be the best choice for fault localization.
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Paper Nr: 72
Title:

VP-IAFSP: Vulnerability Prediction Using Information Augmented Few-Shot Prompting with Open Source LLMs
Authors:

Mithilesh Pandey and Sandeep Kumar
Abstract: Software vulnerabilities can cause significant damage to the organization and the user. This makes their timely and accurate detection pivotal during the software development and deployment process. Recent trends have highlighted the potential of Large Language Models for software engineering tasks and vulnerability prediction. However, their performance is often inhibited if they rely solely on plain text source code. This overlooks the critical syntactic and semantic information present in the code. To address this challenge, we introduce VP-IAFSP(Vulnerability Prediction using Information Augmented Few Shot Prompting). Our approach improves the LLMs’ efficiency for vulnerability prediction through Prompt Enhancements by augmenting information related to the code and integrating graph structural information from the code to utilize Few-shot Prompting. To assess the proposed approach, we conduct experiments on a manually labeled real-world dataset. The results reveal that the proposed methodology achieves between 2.69% to 75.30% increase in F1-Score for function-level vulnerability prediction tasks when compared to seven state-of-the-art methods. These findings underscore the benefits of combining Information Augmentation with Few-shot Prompting while designing prompts for vulnerability prediction.
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Paper Nr: 85
Title:

The Impact of the European Product Liability Directive on Software Engineering
Authors:

Doriana Cobârzan, Richard Bubel and Torsten Ullrich
Abstract: The European Commission’s revised Product Liability Directive was signed in October 2024 and will come into force in 2026. The revision extends the concept of a product to include software and software-based services, and significantly strengthens the legal rights of customers in the event of damage caused by software. This makes liability issues a key aspect of software development. The precise manner in which national legislation is to be drafted and interpreted remains to be clarified. However, the general direction has been sufficiently outlined to enable the implementation of preventative measures, which are discussed briefly here. This article looks at the legal implications of the directive for software producers, focusing on third-party components. It also discusses guidelines to ensure high software quality and improve the legal position of the producer. The present work is concerned exclusively with the Product Liability Directive, notwithstanding its embedding within a framework of regulations, including, for example, the AI Act and the General Data Protection Regulation.
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Paper Nr: 20
Title:

Best Practices and Evaluation Methods for Narrative Information Visualizations: A Systematic Review
Authors:

Andrea Lezcano Airaldi, Emanuel Irrazábal and Andrés Diaz Pace
Abstract: Evaluating narrative visualizations presents unique challenges due to their integration of data and storytelling elements, making traditional assessment methods insufficient. This paper presents a systematic mapping study (SMS) aimed at identifying best practices for designing visualizations and the current evaluation methods used to assess them. It synthesizes 116 studies from 1984 to 2024, examining both traditional information visualizations and narrative visualizations. The study reveals that the application of best practices is highly context-dependent, with trade-offs between simplicity and comprehensiveness. Furthermore, it highlights the lack of standardized evaluation frameworks for narrative visualizations, as existing methods often fail to capture narrative elements. The paper contributes by offering a synthesis of design guidelines, laying the groundwork for future research focused on improving the evaluation of narrative visualizations.
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Paper Nr: 55
Title:

Automated Testing of Tezos Blockchain-Oriented Software
Authors:

Afef Jmal Maâlej and Achraf Weli
Abstract: This research centers on the testing of smart contracts within the Tezos blockchain as a security verification and validation activity, supporting thus its development life cycle. This specific blockchain type is recognized for its self-modifying feature, which facilitates protocol upgrades without network splits. Despite Tezos advanced technology, smart contracts may still harbor bugs and vulnerabilities, necessitating rigorous software testing for quality and security assurance. The primary aim of this work is to develop a solution addressing the divide between technical blockchain development and non-technical participants in the smart contract ecosystem. Our ST2A testing tool, realized for SmartPy-developed smart contracts, offers a user-friendly platform catering to individuals with limited blockchain or programming knowledge. Its overarching objective is to illustrate the Tezos smart contract testing process, ensuring accessibility, comprehension, and actionable insights for non-developers such as project managers, security auditors, and business stakeholders.
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Paper Nr: 87
Title:

Third-Party Library Recommendations Through Robust Similarity Measures
Authors:

Abhinav Jamwal and Sandeep Kumar
Abstract: This research systematically investigates the impact of different similarity measurements on third-party library (TPL) recommendation systems. By assessing the metrics of average precision (MP), average recall (MR), average F1 score (MF), average reciprocal rank (MRR) and average precision (MAP) at different levels of sparsity, the research demonstrates the significant impact of similarity measurements on recommendation performance. Jaccard similarity consistently outperformed the measurements tested and performed better in low-order and high-order app-library interactions. Its ability to reduce the number of sparse data sets and achieve a balance between precision and recall makes it the optimal measurement for the TPL recommendation. Other measurements, such as Manhattan, Minkowski, Cosine, and Dice, exhibited limitations to a certain extent, most importantly under sparse conditions. This research provides a practical understanding of the strengths and weaknesses of similarity measurements, which provides a basis for optimizing the TPL recommendation system in practice.
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Paper Nr: 107
Title:

Exploring the Influence of User Interface on User Trust in Generative AI
Authors:

Morteza Ahmadianmanzary and Sofia Ouhbi
Abstract: As generative AI tools become increasingly integrated into everyday applications, understanding the impact of user interface (UI) design elements on user trust is essential for ensuring effective human-AI interactions. This paper examines how variations in UI design, particularly avatars and text fonts, influence user trust in generative AI tools. We conducted an experiment using the Wizard of Oz method to assess trust levels across three different UI variations of ChatGPT. Nine volunteer university students from diverse disciplines participated in the study. The results indicate that participants’ trust levels were influenced by the generative AI tool’s avatar design and text font. This paper highlights the significant impact of UI design on trust and emphasizes the need for a more critical approach to evaluating trust in generative AI tools.
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Area 4 - Theory and Practice of Systems and Applications Development

Full Papers
Paper Nr: 28
Title:

Exploring and Evaluating Interplays of BPpy with Deep Reinforcement Learning and Formal Methods
Authors:

Tom Yaacov, Gera Weiss, Adiel Ashrov, Guy Katz and Jules Zisser
Abstract: We explore and evaluate the interactions between Behavioral Programming (BP) and a range of Artificial Intelligence (AI) and Formal Methods (FM) techniques. Our goal is to demonstrate that BP can serve as an abstraction that integrates various techniques, enabling a multifaceted analysis and a rich development process. Specifically, the paper examines how the BPpy framework, a Python-based implementation of BP, is enhanced by and enhances various FM and AI tools. We assess how integrating BP with tools such as Satisfiability Modulo Theory (SMT) solvers, symbolic and probabilistic model checking, and Deep Reinforcement Learning (DRL) allow us to scale the abilities of BP to model complex systems. Additionally, we illustrate how developers can leverage multiple tools within a single modeling and development task. The paper provides quantitative and qualitative evidence supporting the feasibility of our vision to create a comprehensive toolbox for harnessing AI and FM methods in a unified development framework.
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Paper Nr: 29
Title:

SQL vs NoSQL: Six Systems Compared
Authors:

Martin Čorovčák and Pavel Koupil
Abstract: The rise of Big Data has exposed the limitations of relational databases in handling large datasets, driving the growth of NoSQL databases. Today, various database systems based on distinct models – or their combinations – are available, raising the question of which is best suited for a specific use case. While several papers compare subsets of these systems, they are often limited in scope. In this paper, we offer a comprehensive comparison of six systems, representing all major data models, through both static and dynamic analysis. We demonstrate their strengths and weaknesses across several realistic use cases.
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Paper Nr: 32
Title:

Agile Effort Estimation Improved by Feature Selection and Model Explainability
Authors:

Víctor Pérez-Piqueras, Pablo Bermejo López and José A. Gámez
Abstract: Agile methodologies are widely adopted in the industry, with iterative development being a common practice. However, this approach introduces certain risks in controlling and managing the planned scope for delivery at the end of each iteration. Previous studies have proposed machine learning methods to predict the likelihood of meeting this committed scope, using models trained on features extracted from prior iterations and their associated tasks. A crucial aspect of any predictive model is user trust, which depends on the model’s explain-ability. However, an excessive number of features can complicate interpretation. In this work, we propose feature subset selection methods to reduce the number of features without compromising model performance. To ensure interpretability, we leverage state-of-the-art explainability techniques to analyze the key features driving model predictions. Our evaluation, conducted on five large open-source projects from prior studies, demonstrates successful feature subset selection, reducing the feature set to 10% of its original size without any loss in predictive performance. Using explainability tools, we provide a synthesis of the features with the most significant impact on iteration performance predictions across agile projects.
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Paper Nr: 49
Title:

Multi-Perspective Analyses of Spatio-Temporal Data About Well-Being
Authors:

Yunji Zhang, Franck Ravat, Sébastien Laborie and Philippe Roose
Abstract: The concept of ”Well-being” within local territories is increasingly recognized as a critical issue by local decision-makers. In the face of demographic shifting and population ageing, decision-makers need to anticipate demographic changes, plan land use, and shift land use promptly. They need a broader perspective that integrates various dimensions of the living environment for their territories. Therefore, it requires a system that can integrate different datasets and perspectives on various dimensions of ”Well-being”, including demographics, population distribution, land utilisation, transport, infrastructure development, social and business services, etc. It can perform comprehensive multi-perspective analyses based on integrated perspectives. However, the existing work on this topic mainly focuses on a single-perspective analysis, such as focusing exclusively on education. In order to fill this gap, this article aims to propose: (i) a mind map outlining the dimensions related to ”Well-being” and the associated data required for analyses; (ii) an on-read schema modelling framework for the storage, the cross-integration and the promoting accessibility of the multi-perspective data; and (iii) a modelling concept for multi-perspective analysis data to represent the various dimensions relating to ”Well-being”.
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Paper Nr: 51
Title:

Supporting Automated Documentation Updates in Continuous Software Development with Large Language Models
Authors:

Henok Birru, Antonio Cicchetti and Malvina Latifaj
Abstract: Nowadays software is ubiquitous in our society, making its role increasingly mission critical. Software applications need to be continuously maintained and evolved to keep up with the pace of market demands and emerging issues. Continuous Software Development (CSD) processes are an effective technological countermeasure to the mentioned evolutionary pressures: practices like DevOps leverage advanced automation mechanisms to streamline the application life-cycle. In this context, while handling the application development and implementation is adequately investigated, managing the continuous refinement of the corresponding documentation is a largely overlooked issue. Maintaining accurate technical documentation in CSD is challenging and time-consuming because the frequent software changes require continuous updates and such a task is handled manually. Therefore, this work investigates the automation of documentation updates in correspondence with code changes. In particular, we present CodeDocSync, an approach that uses Large Language Models (LLMs) to automate the updating of technical documentation in response to source code changes. The approach is developed to assist technical writers by summarizing code changes, retrieving updated content, and allowing follow-up questions via a chat interface. The approach has been applied to an industrial scenario and has been evaluated by using a set of well-known predefined metrics: contextual relevancy, answer relevancy, and faithfulness. These evaluations are performed for the retriever and generator components, using different LLMs, embedding models, temperature settings, and top-k values. Our solution achieves an average answer relevancy score of approximately 0.86 with Ope-nAI’s gpt-3.5-turbo and text-embedding-3-large. With an emotion prompting technique, this score increases to 0.94, testifying the viability of automation support for continuous technical documentation updates.
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Paper Nr: 75
Title:

Towards an Approach for Project-Library Recommendation Based on Graph Normalization
Authors:

Abhinav Jamwal and Sandeep Kumar
Abstract: The performance of project-library recommendation systems depends on the choice of graph normalization techniques. This work explores two primary normalization schemes within a knowledge graph - enhanced project - library recommendation system: symmetric normalized Laplacian (SNL) and random walk normalized Laplacian (RWL). Experimental results show that RWL consistently delivers better performance in key metrics, including mean precision (MP), mean recall (MR), and mean F1 score (MF), particularly in sparse datasets. Although SNL performs well in denser datasets, its effectiveness decreases with increasing sparsity. Furthermore, loss curves for collaborative filtering (CF) and knowledge graph (KG) tasks indicate that RWL converges faster and shows greater stability. These findings establish RWL as a reliable technique for improving GNN-based recommendation systems, especially in sparse and complex project-library interaction scenarios.
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Paper Nr: 86
Title:

Automated Social Media Feedback Analysis for Software Requirements Elicitation: A Case Study in the Streaming Industry
Authors:

Melissa Silva and João Pascoal Faria
Abstract: Requirements Engineering (RE) is crucial for product success but challenging for software with a broad user base, such as streaming platforms. Developers must analyze vast user feedback, but manual methods are impractical due to volume and diversity. This research addresses these challenges by automating the collection, filtering, summarization, and clustering of user feedback from social media, suggesting feature requests and bug fixes through an interactive platform. Data from Reddit, Twitter, iTunes, and Google Play is gathered via web crawlers and APIs and processed using a novel combination of natural language processing (NLP), machine learning (ML), large language models (LLMs), and incremental clustering. We evaluated our approach with a partner company in the streaming industry, extracting 66,168 posts related to 10 streaming services and identifying 22,847 as relevant with an ML classifier (75.5% precision, 74.2% recall). From the top 100 posts, a test user found 89 relevant and generated 47 issues in 80 minutes—a significant reduction in effort compared to a manual process. A usability study with six specialists yielded a SUS score of 83.33 (“Good”) and very positive feedback. The platform reduces cognitive overload by prioritizing high-impact posts and suggesting structured issue details, ensuring focus on insights while supporting scalability.
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Paper Nr: 89
Title:

LLM-Generated Microservice Implementations from RESTful API Definitions
Authors:

Saurabh Chauhan, Zeeshan Rasheed, Abdul Malik Sami, Zheying Zhang, Jussi Rasku, Kai-Kristian Kemell and Pekka Abrahamsson
Abstract: The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices. This system assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages. By focusing on the API-first methodology, this system ensures that microservices are designed with well-defined interfaces, promoting consistency and reliability across the development life-cycle. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This process automates the generation of microservices and also simplifies the debugging and refinement phases, allowing developers to focus on higher-level design and integration tasks. This system has the potential to benefit software developers, architects, and organizations to speed up software development cycles and reducing manual effort. To assess the potential of the system, we conducted surveys with six industry practitioners. After surveying practitioners, the system demonstrated notable advantages in enhancing development speed, automating repetitive tasks, and simplifying the prototyping process. While experienced developers appreciated its efficiency for specific tasks, some expressed concerns about its limitations in handling advanced customizations and larger-scale projects. The code is publicly available at https://github.com/sirbh/code-gen.
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Paper Nr: 92
Title:

Autonomous Legacy Web Application Upgrades Using a Multi-Agent System
Authors:

Valtteri Ala-Salmi, Zeeshan Rasheed, Abdul Malik Sami, Zheying Zhang, Kai-Kristian Kemell, Jussi Rasku, Shahbaz Siddeeq, Mika Saari and Pekka Abrahamsson
Abstract: The use of Large Language Models (LLMs) for autonomously generating code has become a topic of interest in emerging technologies. As the technology improves, new possibilities for LLMs use in programming continue to expand such as code refactoring, security enhancements, and legacy application upgrades. Nowadays, a large number of web applications on the internet are outdated, raising challenges related to security and reliability. Many companies continue to use these applications because upgrading to the latest technologies is often a complex and costly task. To this end, we proposed LLM based multi-agent system that autonomously upgrade the legacy web application into latest version. The proposed multi-agent system distributes tasks across multiple phases and updates all files to the latest version. To evaluate the proposed multi-agent system, we utilized Zero-Shot Learning (ZSL) and One-Shot Learning (OSL) prompts, providing the same instructions for both. The evaluation process was conducted by updating a number of view files in the application and counting the amount and type of errors in the resulting files. In more complex tasks, the amount of succeeded requirements was counted. The prompts were run with the proposed system and with the LLM as a standalone. The process was repeated multiple times to take the stochastic nature of LLM’s into account. The result indicates that the proposed system is able to keep context of the updating process across various tasks and multiple agents. The system could return better solutions compared to the base model in some test cases. Based on the evaluation, the system contributes as a working foundation for future model implementations with existing code. The study also shows the capability of LLM to update small outdated files with high precision, even with basic prompts. The code is publicly available on GitHub: https://github.com/alasalm1/ Multi-agent-pipeline.
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Paper Nr: 94
Title:

Software Sustainability Perceptions in the Industry: A Questionnaire Study
Authors:

Jennifer Gross, Temesgen Hagos Mengesha and Sofia Ouhbi
Abstract: Despite growing awareness, many industry efforts regarding software sustainability often appear superficial and fail to address its complex, multifaceted nature. This paper examines software sustainability practices in the industry through the perspectives of IT practitioners. A questionnaire study involving 23 professionals based in Sweden revealed a significant gap in understanding, with 35% of participants unfamiliar with the term “software sustainability.” Most definitions provided by participants focused on technical aspects, overlooking economic and social dimensions. The findings indicate that key barriers perceived by participants to integrating sustainability include a lack of awareness, time and budget constraints, and skepticism toward sustainability metrics. A majority of respondents recognized the link between sustainability and software quality. To promote sustainable software practices, respondents recommended embedding sustainability into industry practices and educational curricula, as well as developing clear metrics to measure its impact.
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Paper Nr: 101
Title:

S4BP: An Approach for Assessing Business Process Stability
Authors:

Hajer Ben Haj Ayech, Ricardo Martinho and Sonia Ayachi Ghannouchi
Abstract: Achieving business process (BP) stability is a fundamental objective for organizations, pursued for a variety of reasons including consistency in operations and product/service delivery, reduced costs and rework, and clear metrics for process improvement. Nevertheless, the subject has received little attention in research, from vague definitions to mingled concepts involving BP flexibility and changes. This paper addresses the stability of BP in the context of Business Process Management (BPM). Specifically, it proposes a clearer definition of BP stability, as well as a step-by-step Stability for Business Processes approach (S4BP) based on Process Mining techniques to evaluate and predict stability for a certain BP. The proposed approach is demonstrated through a software implementation in the form of a ProM plugin, and validated using a case study with public datasets from the Business Process Improvement (BPI) Challenge.
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Paper Nr: 105
Title:

Combining Goal and Process Models for the Specification of Human-Robot Collaborations
Authors:

Shaza Elbishbishy, Jeshwitha Jesus Raja, Philipp Kranz and Marian Daun
Abstract: Human-Robot collaboration enhances flexibility and efficiency in modern manufacturing. Collaborative robots work alongside human operators to combine robotic precision with human adaptability. A key challenge is defining task sequences, managing dependencies, and aligning workflows with strategic goals. This paper addresses this challenge by integrating goal models with process models. Goal models capture the “why,” while process models define the “how” in operational workflows. Our approach systematically maps goals to tasks, ensuring clear traceability and cohesive task execution. We evaluate this method through a collaborative assembly use case. The integration refines task dependencies, improves coordination, and ensures alignment between goals and processes. This approach supports efficient human-robot collaboration in semi-automated manufacturing environments.
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Paper Nr: 116
Title:

Exploring Feature Extraction Techniques and SVM for Facial Recognition with Image Generation Using Diffusion Models
Authors:

Nabila Daly, Faten Khemakhem and Hela Ltifi
Abstract: Facial recognition is a cornerstone of computer vision, with applications spanning security, personalization, and beyond. In this study, we enhance the widely used Labeled Faces in the Wild (LFW) dataset by generating additional images using a diffusion model, enriching its diversity and volume. These augmented datasets were then employed to train Support Vector Machine (SVM) classifiers using three distinct feature extraction methods: Histogram of Oriented Gradients (HOG), Eigenfaces, and Local Binary Patterns (LBP), in combination with SVM (HOG-SVM, Eigenfaces-SVM, and LBP-SVM). Our investigation evaluates the impact of these hybrid approaches on facial recognition accuracy and computational efficiency when applied to the expanded dataset. Experimental results reveal the strengths and limitations of each method, providing valuable insights into the role of feature extraction and data augmentation in improving facial recognition systems.
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Paper Nr: 118
Title:

A Study on the Comprehensibility of Behavioral Programming Variants
Authors:

Adiel Ashrov, Arnon Sturm, Achiya Elyasaf and Guy Katz
Abstract: Behavioral Programming (BP) is a software engineering paradigm for modeling and implementing complex reactive systems. BP’s goal is to enable developers to incrementally model systems in a manner naturally aligned with their perception of the system’s requirements. This study compares two BP variants: classical, context-free BP, and the more recently proposed Context-Oriented BP (COBP). While BP advocates simplicity and modularity, COBP introduces context-aware constructs for handling context-dependent behaviors. A practical question arises: which variant leads to reactive systems that are more comprehensible for developers? Through a controlled experiment with 109 participants, we evaluated both variants across two dimensions: comprehension of execution semantics and identification of requirements from implementations. The results indicate that BP generally leads to better comprehension and higher confidence; while COBP demonstrates advantages in complex, context-dependent behaviors. These findings provide guidance for choosing between BP variants based on system complexity and context-dependent requirements.
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Paper Nr: 120
Title:

Identifying Testing Behaviour in Open Source Projects: A Case Analysis for Apache Spark
Authors:

Asli Sari, Ayse Tosun and Gülfem Işıklar Alptekin
Abstract: Open Source Software (OSS) projects have the potential to achieve high software quality through community collaboration. However, the collaborative nature of OSS development presents unique challenges, particularly in maintaining software quality through testing practices. The lack of formal testing roles and structures underscores the importance of understanding testing patterns to enhance project quality. To address this need, our study investigates key aspects of testing contributions within Apache Spark project. The study aims to identify the top testing contributors responsible for the majority of test-related commits, as well as their engagement levels and evolving testing focus over time. Additionally, it examines how these contributors’ activities vary across different time periods and explores their distinct engagement patterns within the community. Our findings reveal that only 9.8% of contributors handle the majority of test-related commits, exceeding the traditional 80/20 Pareto principle. Additionally, hierarchical clustering of these contributors over three years identified three activity levels: Highly-Active, Moderately-Active, and Lowly-Active. Each cluster exhibits unique patterns of testing focus and engagement across different time periods. These insights emphasize the critical role of a small core group in managing the project’s testing workload and underscore the need for strategies to broaden participation in testing activities.
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Paper Nr: 131
Title:

Information-Theoretic Patient Record Matching in Medical Databases: A Discriminative Power and Feature Analysis Using MIMIC-IV
Authors:

Vitalijs Teze, Erika Nazaruka and Dmirtijs Bliznuks
Abstract: This paper presents an information-theoretic framework to evaluate feature discriminative power and stability for patient record matching. We analyse the discriminative power and temporal stability of features through Shannon entropy, evaluating their effectiveness for patient identification without unique identifiers. Our framework categorizes features into demographics/administrative (𝐷(𝐹)=12247.56 bits), ICU care patterns (𝐷(𝐹)=266.40 bits), and clinical records (𝐷(𝐹)=12.10 bits), achieving a combined discriminative power of 12526.06 bits. This significantly exceeds the theoretical minimum threshold (logଶ(𝑁) ≈ 16 bits) for our population of 65,366 patients. The framework employs hierarchical feature weighting based on information content and stability coefficients, revealing that temporal patterns and service transitions contain higher discriminative power than traditional demographic identifiers. We demonstrate that effective matching requires balancing feature stability against information content while maintaining computational efficiency. The framework provides a foundation for implementing reliable patient matching systems, though further validation across diverse healthcare environments is needed.
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Paper Nr: 134
Title:

A Heuristic Approach to Localize CSS Properties for Responsive Layout Failures
Authors:

Tasmia Zerin, B. M. Mainul Hossain and Kazi Sakib
Abstract: Responsive Layout Failures (RLFs) typically arise from CSS properties that hinder proper layout behavior in different screen sizes. To find an accurate and effective solution for repairing RLFs, localization of those problematic properties is necessary. However, existing approaches only detect RLFs and apply broad CSS patches for them. The patches alter the entire layout without localizing the root cause of failure. To address this gap, we propose a heuristic approach to identify the specific CSS properties that developers would typically localize manually. The approach first detects the RLFs existing in a webpage and their affected elements. Next, it localizes the nearby HTML elements using RLF direction and relative alignment of the elements present in the RLF region. The involved CSS properties of those elements are then identified using a ranked search set of CSS properties, created by analyzing Quora and Stack Overflow queries. Finally, elements and their corresponding property pairs are ranked based on their impact on RLFs. We have implemented this approach into a tool called LOCALICSS and evaluated it on a set of webpages using Top N Rank, MRR and P@K metrics. The tool achieved localization accuracy ranging from 45.2% (Top-1) to 92.86% (Top-7), with an MRR of 76% and a P@3 of 77.13%. Additionally, experienced front-end engineers manually localized the RLFs as part of our evaluation. Their preferred CSS properties matched the suggestions from our approach in 42.86% of cases for Top-1 rankings and up to 90.48% for Top-7 rankings.
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Paper Nr: 142
Title:

Model-Driven Development Using LLMs: The Case of ChatGPT
Authors:

Virginia Niculescu, Maria-Camelia Chisăliță-Crețu, Cristina-Claudia Osman and Adrian Sterca
Abstract: The recent rise of Large Language Models (LLMs) suggests the possibility for users with different levels of expertise to generate software applications from high-level specifications such as formatted text, diagrams or natural language. This would enhance productivity and make these activities accessible to users without a technical background. Approaches such as Model-Driven Engineering (MDE) and Workflow Management Systems (WfMSs) are widely used to enhance productivity and streamline software development through automation. This study explores the feasibility of using LLMs, specifically ChatGPT, in software development, focusing on their capability to assist business analysts (BAs) in generating functional applications. The goal of this paper is threefold: (1) to assess the extent to which LLMs comprehend conceptual model diagrams, (2) to evaluate the reliability of diagram-based code generation, and (3) to determine the level of technical knowledge required for users to achieve viable solutions. Our methodology evaluates the effectiveness of using LLMs to generate functional applications starting from BPMN process diagrams and Entity-Relationship (ER) diagrams. The findings provide insights into the reliability and limitations of LLMs in diagram-based software generation, the degree of technical expertise required, and the prospects for adopting LLMs as tools for BAs.
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Short Papers
Paper Nr: 12
Title:

Advancing Network Anomaly Detection Using Deep Learning and Federated Learning in an Interconnected Environment
Authors:

Hanen Dhrir, Maha Charfeddine and Habib M. Kammoun
Abstract: Network anomaly detection is a fundamental cybersecurity task that seeks to identify unusual patterns that could indicate security threats or system failures. Traditional centralized anomaly detection methods face issues such as data privacy. Federated Learning has emerged as a promising solution that distributes model training across multiple devices or nodes. Federated Learning improves anomaly detection by leveraging geographically distributed data sources while maintaining data privacy and security. This study presents a novel Federated Learning architecture designed specifically for network anomaly detection, addressing important information sensitivity issues in network environments. We compare some Deep Learning algorithms, such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Multilayer Perceptron (MLP), using XGBoost for feature selection and Stochastic Gradient Descent (SGD) as an optimizer. To address the problem of imbalanced data, we use the Synthetic Minority Over-sampling Technique (SMOTE) with the UNSW-NB15 dataset. Our methodology is rigorously evaluated using standard evaluation metrics and compared to state-of-the-art approaches.
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Paper Nr: 17
Title:

Impact of Business Process Masking on Organizations' Policies
Authors:

Zakaria Maamar, Amel Benna, Hirad Rezaei, Amin Beheshti and Fethi Rabhi
Abstract: To preserve their competitiveness, organizations that engage in partnership have the opportunity of masking their business processes without undermining this partnership’s progress. Aggregation and abstraction correspond to masking where the former groups activities together giving the impression of a limited number of activities in a business process, and the latter makes some activities invisible since they are deemed not relevant for partnership. Besides masking, organizations adopt policies to define permissions, prohibitions, and obligations on business processes at run-time. This paper examines the impact of business process masking on policies with focus on adjusting, dropping, and developing policies in Open Digital Rights Language (ODRL). A system demonstrating this impact is also presented in the paper.
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Paper Nr: 36
Title:

Exploring the Role of Service Design in Software Development: A Systematic Mapping
Authors:

Henrique Pucci Pinto, Júlia de Souza, Elaine Venson and Rejane da Costa Figueiredo
Abstract: As digital services gain prominence in our society, service design has become a valuable approach for supporting the development of complex software applications that interact with multiple stakeholders. This paper presents a systematic mapping study to offer an overview of the literature regarding the use of the service design approach in driving software development. We analyzed 30 papers focusing on the utilization of service design techniques, along with its benefits. Our findings show a wide range of references to service design, but few focus on the process itself; most only mention the approach. Service design, as our analysis highlights, bridges communication gaps in projects and ensures user needs are fully met. We compiled methodologies and techniques commonly used in the service design process. In conclusion, applying service design to guide software development is an unexplored approach with significant potential. The literature gap highlights the need for further research to explore its implications and benefits.
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Paper Nr: 53
Title:

Detecting Duplicate Effort in GitHub Contributions
Authors:

James Galbraith and Des Greer
Abstract: The pull-based development model allows collaborators to develop and propose changes to a codebase. However, pull requests can often offer duplicate functionality and therefore duplicate effort. Users can also request changes via issues, the text of which could provide clues, useful in determining duplicate pull requests. This research investigates combining pull requests with issues with a view to better detecting duplicate pull requests. The paper reviews existing related work and then extends this by investigating the use of natural language processing (NLP) on combined issues and pull requests in order to detect duplicates. Using data taken from 15 popular GitHub repositories, an NLP model was trained to predict duplicates by comparing the title and description of issues and pull requests. An evaluation of this model shows that duplicates can be detected with an accuracy of 93.9% and recall rate of 90.5%, while an exploratory study shows that the volume of duplicates detected can be increased dramatically by combining issues and pull requests into a single dataset. These results show a significant improvement on previous studies and demonstrate the value in detecting duplicates from issues and pull requests combined.
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Paper Nr: 54
Title:

Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-Being
Authors:

Esperança Amengual-Alcover, Antoni Jaume-i-Capó, Miquel Miró-Nicolau, Gabriel Moyà-Alcover and Antonia Paniza-Fullana
Abstract: The integration of Artificial Intelligence in the development of computer systems presents a new challenge: make intelligent systems explainable to humans. This is especially vital in the field of health and well-being, where transparency in decision support systems enables healthcare professionals to understand and trust automated decisions and predictions. To address this need, tools are required to guide the development of explainable AI systems. In this paper, we introduce an evaluation framework designed to support the development of explainable AI systems for health and well-being. Additionally, we present a case study that illustrates the application of the framework in practice. We believe that our framework can serve as a valuable tool not only for developing explainable AI systems in healthcare but also for any AI system that has a significant impact on individuals.
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Paper Nr: 58
Title:

Automated Migration of Legacy Code from the C++14 to C++23 Standard
Authors:

Aleksander Świniarski and Anna Derezińska
Abstract: The continuous development of the C++ programming language results in changes in many programming features from one version to another. Therefore, we face a growing increase in maintenance and evolution costs. To address this problem, a set of removed and deprecated programming features was examined, and automating of the feature migration was proposed. A transpiler has been developed that transforms a C++ code from a legacy form to its latest standard. The CppUp tool translates a C++14 program into its equivalent C++23. The current version of the tool supports 17 removed and 3 deprecated features. The restrictions of the tool limit its practical application, but the experiments conducted on seven real-world programs confirmed the reliability and usability of the transpiler.
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Paper Nr: 62
Title:

AADT: Asset-Driven Attack-Defense Tree
Authors:

Nan Messe and Avi Shaked
Abstract: Attack trees are widely used in threat modelling and risk analysis to describe threats and identify attacks that realize them. Their extension, attack-defense trees, allows for depicting interactions between attackers and defenders. However, current graphical representations of attack(-defense) trees focus primarily on threat scenarios and do not account for the representation of domain elements and their hierarchical organization. Com-promised domain elements (e.g., systems, sub-systems, components, etc.) are thus not directly highlighted in any of these tree representations, which requires additional effort from decision-makers during impact analysis. To help make impact analysis more explicit and enable stakeholders to assign and evaluate security controls more effectively, we propose a novel methodology for graphical secure system modelling and assessment, the “Asset-Driven Attack-Defense Tree” (AADT). AADT is an extension of the attack-defense tree, combining the security and system views for seamless secure system development. AADT’s main contribution lies in bridging the system and security views while representing domain elements, associated vulnerabilities, and security controls at different levels of abstraction, which is aligned with the system development lifecy-cle. This layered representation is especially useful in the fast-evolving cyber threat landscape, where diverse attack techniques often exploit similar vulnerabilities. By associating vulnerability categories with domain elements and proposing high-level security controls, AADT helps stakeholders manage a broad spectrum of attacks targeting similar vulnerabilities, thus enabling a more proactive and structured approach to secure system development. We also present a formalism for AADT and illustrate the AADT methodology using a simple, real-world scenario based on the existing security body of knowledge.
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Paper Nr: 68
Title:

Licy: A Chatbot Assistant to Better Understand and Select Open Source Software Licenses
Authors:

Giorgos Shittas, Georgia M. Kapitsaki and Maria Papoutsoglou
Abstract: Open Source Software (OSS) carries licenses that specify the terms under which the software is made available for use. Various resources are available for software engineers online, in order to assist them in understanding and choosing among the available OSS licenses when creating their software projects. However, these resources lack in the provision of a sense of interactivity to user prompts, which would have been useful for providing guidance in a more familiar manner. In this work, we present our approach for Licy, a chatbot OSS licensing assistant for guiding users with information on specific OSS licenses and for choosing which OSS licenses to use in specific cases. A large number of licenses are supported by the chatbot using the license model offered by choosealicense, focusing on license permissions, limitations and conditions. We describe the design and implementation process of the chatbot and its preliminary evaluation results using chatbot design metrics and a user evaluation. We argue that the chatbot can serve as a starting point for similar interactive assistants for software engineers, and describe its value in that respect.
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Paper Nr: 69
Title:

A Replicated Study on Factors Affecting Software Understandability
Authors:

Georgia M. Kapitsaki, Luigi Lavazza, Sandro Morasca and Gabriele Rotoloni
Abstract: Background. Understandability is an important characteristic of software code that can largely impact the effectiveness and cost of software maintenance. Aim. We investigate if and to what extent the characteristics of source code captured by static metrics affect understandability. Method. We replicated an empirical study which provided some insights and highlighted some code characteristics that seem to affect understandability. The replication took place in a different country and was conducted with a different set of developers, i.e., Bachelor’s students, instead of Master’s students. The same source code was used in both studies. Results. The data collected in the replication do not corroborate the results of the initial study, since no correlation between code measures and code understanding could be found. The reason seems to be that the initial study involved developers with very similar skills and experience, while the replication involved developers with quite different skills. Conclusions. Code understanding appears to be affected much more by developers’ skills than by code characteristics. The extent to which code understanding depends on code characteristics is observable only for a homogeneous population of developers. Our results can be useful for software practitioners and for future software understandability studies.
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Paper Nr: 79
Title:

Medical Chatbot for Disease Prediction Using Machine Learning and Symptom Analysis
Authors:

Oltean Anisia Veronica, Ioan Daniel Pop and Adriana Mihaela Coroiu
Abstract: This paper emphasizes the transformational role of artificial intelligence in the medical field by studying not only various machine learning algorithms used for symptoms-based disease prediction, but also methods used in conversational artificial intelligence. At its core, the research was carried out as a first step in the development of a medical chatbot that allows patients to receive diagnosis and advice related to various diseases and their possible treatments. In our paper, various machine learning algorithms were compared for predicting diseases based on symptoms, such as Logistic Regression, Random Forests, Decision Trees, Naive Bayes and Multilayer Perceptron, which were evaluated on multiple datasets. Given the lack of publicly available datasets for such a task, a final dataset was generated, achieving satisfactory accuracy values of approximately 80%.
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Paper Nr: 82
Title:

Variability-Driven User-Story Generation Using LLM and Triadic Concept Analysis
Authors:

Alexandre Bazin, Alain Gutierrez, Marianne Huchard, Pierre Martin and Yulin Zhang
Abstract: A widely used Agile practice for requirements is to produce a set of user stories (also called “agile product backlog”), which roughly includes a list of pairs (role, feature), where the role handles the feature for a certain purpose. In the context of Software Product Lines, the requirements for a family of similar systems is thus a family of user-story sets, one per system, leading to a 3-dimensional dataset composed of sets of triples (system, role, feature). In this paper, we combine Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompting to suggest the user-story set required to develop a new system relying on the variability logic of an existing system family. This process consists in 1) computing 3-dimensional variability expressed as a set of TCA implications, 2) providing the designer with intelligible design options, 3) capturing the designer’s selection of options, 4) proposing a first user-story set corresponding to this selection, 5) consolidating its validity according to the implications identified in step 1, while completing it if necessary, and 6) leveraging LLM to have a more comprehensive website. This process is evaluated with a dataset comprising the user-story sets of 67 similar-purpose websites.
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Paper Nr: 103
Title:

A Progressive Step Towards Automated Fact-Checking by Detecting Context in Diverse Languages: A Prototype for Bangla Facebook Posts
Authors:

Mahbuba Shefa, Tasnuva Ferdous, Afzal Azeem Chowdhary, Md Jannatul Joy, Tanjila Kanij and Md Al Mamun
Abstract: Fact-Checking has become a critical tool in combating misinformation, particularly on platforms like Face-book, where the rapid spread of false information poses significant challenges. Much work has been done on languages like English but not on low-resource languages like Bangla. To address this gap, we explored the application of classic ML models, RNNs, and BanglaBERT on a small dataset of Bangla Facebook textual posts to understand its context. Surprisingly, BanglaBERT underperformed compared to traditional approaches like models based on TF-IDF embeddings, highlighting the challenges of working with limited data and insufficient fine-tuning. To support fact-checkers, we developed the “Automated Context Detector,” which is developed with NLP and machine learning that automates repetitive tasks, allowing experts to focus on critical decisions. Our results demonstrate the feasibility of using machine learning for context detection in Bangla social media posts, providing a framework adaptable to similar linguistic and cultural settings.
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Paper Nr: 114
Title:

A Near-Optimal Steganography-Based Solution for Embedding Data in Video Cover Medium
Authors:

Ali Mohammed Abed, Houcemeddine Hermassi and Walid Barhoumi
Abstract: Nowadays, videos are the most popular and convenient way to communicate online; this is due to the ease with which video processing software is accessible online. Video steganography is the process of embedding data into video while maintaining the video’s visual quality. The media that makes it feasible to move data quickly between locations is the internet. However, sending data over the internet is extremely dangerous. Therefore, the steganography technology is utilized to protect privacy and stop unauthorized individuals from retrieving information. Steganography is the process of concealing private data, including audio, video, images, and text. The text, image, audio, and video files will all conceal this sensitive information. Video steganography is the process of hiding confidential information in a video file. This study offers a new, effective method that improves state-of-the-art performance, lowers the danger of detection, and is simple. The results of the experiments demonstrate that the suggested method works better than the current techniques. Furthermore, our approach demonstrated notable improvements in communication time via new technologies.
Paper Nr: 130
Title:

Data-Driven Personas for Software Engineering Research
Authors:

Jefferson Seide Molléri and Bogdan Marculescu
Abstract: This paper presents a proof-of-concept on creating data-driven personas for software engineering research using Stack Overflow survey data. We developed three archetypes to illustrate how quantitative data can inform research scenarios. The process involved addressing challenges such as interpreting quantitative data, balancing detail and applicability, ensuring realism, and iterative refinement. The work emphasizes personas as a flexible, human-centered tool that addresses methodological issues in SE research.
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Paper Nr: 132
Title:

Design of a Serious Game on Exploratory Software Testing to Improve Student Engagement
Authors:

Niels Doorn, Tanja E. J. Vos and Beatriz Marín
Abstract: Teaching software testing in computer science education faces challenges due to its abstract nature and students’ focus on approaches using paradigms based on rationalism. Exploratory testing, which uses a paradigm based on empiricism and employs reflective learning, is under-represented in computer science curricula. To address this gap, game-based learning presents promising approaches to enhance engagement and foster critical thinking in software testing education. This position paper presents the design of a serious game to support the teaching of exploratory software testing to improve the students’ engagement. The game integrates software testing tours and uses Socratic questioning as scaffolding to promote deeper reflection-in-action, allowing students to experience hands-on learning in software testing. Using a mapping review, this study identifies the most effective gamification techniques for software testing education and principles of Socratic questioning. Based on these findings, we designed a game that focusses on exploratory testing scenarios, where players follow a tour-based test strategy on a system under test.
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Paper Nr: 133
Title:

An Empirical Framework for Automatic Identification of Video Game Development Problems Using Multilayer Perceptron
Authors:

Pratham Maan, Lov Kumar, Vikram Singh, Lalita Bhanu Murthy and Aneesh Krishna
Abstract: The video game development industry deals with all aspects of video game development, including development, distribution, and monetization. Over the past decade, video game consumption has skyrocketed and the industry has witnessed remarkable technological advances, although it has stumbled across some bottlenecks. The lack of a well-formatted game’s postmortem video is one pivotal issue. A postmortem video is published after the game’s release, to track its development and often understanding ’what went right and what went wrong’. Despite its importance, there is a minimal understanding formal structure of postmortem videos explored to identify video game development-related problems. In this work conducted a systematic analysis of the chosen video game problem dataset extracted from postmortem videos with 1035 problems. We designed Multilayer Perceptron (MLP) classifiers for early identification of video game development problems based on their description or quote. The empirical analysis investigated the effectiveness of 09 MLP-based classification models for identifying video game development problems, using 07-word embedding techniques, 03 feature selection techniques and a class balancing technique. The experimental work confirms the higher predictive ability of MLP compared to traditional ML algorithms such as KNN, SVC, etc, with 0.86 AUC values. Moreover, the effectiveness of class balancing and feature selection techniques for selecting the best feature set is evaluated by box plot and Mean rank test using the Friedman Mean Rank test on the null hypothesis, indicating an impact on the overall predictive ability of MLP models with AUC values of 0.862.
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Paper Nr: 136
Title:

Healthcare Bias in AI: A Systematic Literature Review
Authors:

Andrada-Mihaela-Nicoleta Moldovan, Andreea Vescan and Crina Grosan
Abstract: The adoption of Artificial Intelligence (AI) in healthcare is transforming the field by enhancing patient care, advancing diagnostic precision, and optimizing clinical flows. Despite its promise, algorithmic bias remains a pressing challenge, raising critical concerns about fairness, equity, and the reliability of AI systems in diverse healthcare settings. This Systematic Literature Review (SLR) investigates how bias manifests across the AI lifecycle—spanning data collection, model training, and real-world application and examines its implications for healthcare outcomes. By rigorously analyzing peer-reviewed studies based on inclusion and exclusion criteria, this review identifies the populations most impacted by bias and explores the diversity of existing mitigation strategies, fairness metrics, and ethical frameworks. Our findings reveal persistent gaps in addressing health inequities and underscore the need for targeted interventions to ensure AI systems serve as tools for equitable and ethical care. This work aims to guide future research and inform policy development, in order to prioritize both technological progress and social responsibility in healthcare AI.
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Paper Nr: 144
Title:

A Risk Assessment of Information Security in a Diet Centre Business: A Case Study
Authors:

Tasneem Annahdi, Duaa Alkubaisy and Luca Piras
Abstract: This paper employed the framework of Operationally Critical Threat, Asset, and Vulnerability Evaluation Allegro (OCTAVE-Allegro) to analyse the key risks and challenges faced by the business of Diet Centre X, particularly in terms of security, operational efficiency, and customer trust. The primary concerns identified include data input errors, outdated billing systems, weak password management practices, and a lack of comprehensive security awareness training. These issues pose significant risks to the centre’s productivity, financial health, and reputation. Contributions of this paper include the proposal of several lessons learned and solutions: creating a customer registration system that is connected to the client data validation in the management system, along with implementing a validation for all input fields to reduce human errors and upgrading the billing system to remove outdated payment methods and enhance the user interface, and conducting quarterly security awareness training for all employees to increase their preparedness against potential security threats.
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Paper Nr: 24
Title:

Exploring the Willingness of Nigerian Community Policing Stakeholders to Adopt Technology Through Survey
Authors:

Otuu Obinna Ogbonnia, Henry Anajemba Joseph and Deepak Sahoo
Abstract: Community policing (CP) is widely acknowledged for its role in enhancing safety globally. However, in Nigeria, the effectiveness of CP is diminishing despite advancements in CP technology. This decline highlights a lack of understanding regarding citizens' awareness, concerns, and willingness to adopt technological solutions for CP. Our study investigates Nigerians' awareness and concerns about CP initiatives and their readiness to use technology to support these efforts. By surveying 1200 participants from all six geopolitical zones online, we discovered a significant lack of awareness among citizens about key aspects of CP, despite its potential to reduce crime, improve safety perceptions, and strengthen community-police relationships. Nonetheless, a large majority (86%) expressed willingness to use technology for CP engagements. This research marks the beginning of a Human Computer Interaction study aimed at integrating technology into CP in Nigeria. The findings provide direction for future research phases and offer valuable insights for policymakers to improve law enforcement practices and community engagement strategies in Nigeria.
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Paper Nr: 26
Title:

Recommender Systems Approaches for Software Defect Prediction: A Comparative Study
Authors:

Ahmed-Reda Rhazi, Oumayma Banouar, Fadel Toure and Said Raghay
Abstract: Defects prediction is an important step in the software development life cycle. Projects involving thousands of classes require the writing of unit tests for a significant number of classes, which is a costly and time-consuming process. Some research projects in this area have tried to predict defect-prone classes in order to better allocate testing effort in the relevant components. Algorithms such as neural networks and ensemble learning have been used to classify the project classes. Based on similarities, Recommender systems (RS) allow users to have customized recommendations in different domains, such as social media and e-commerce. This paper explores the usage of recommender systems in the prediction of software defects. Using a dataset of 14 open source systems containing 5883 Java classes, we compared the performance of content-based RS approaches applied to software defect prediction using software metrics as features, with classic classification algorithms such as SVM, KNN, and ensemble learning algorithms. For the Content-based approach, the similarities are computed between software classes first with the standard software metrics and then with PCA (principal component analysis) extracted components. Finally, by aggregating the top-N most similar classes, the approach is capable of predicting whether the current class is defect-prone or not. The comparison is made using the Accuracy, Precision, and F-1 Score. The results show that the recommender systems approach can be a viable alternative to traditional machine learning methods in the classification and prediction of software defect classes.
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Paper Nr: 37
Title:

MODE: A Customizable Open-Source Testing Framework for IoT Systems and Methodologies
Authors:

Rareș Cristea, Ciprian Paduraru and Alin Stefanescu
Abstract: With the growing integration of software and hardware, IoT security solutions must become more efficient to maintain user trust, boost enterprise revenue, and support developers. While fuzzing is a common testing method, few solutions exist for fuzzing an entire IoT application stack. The absence of an open-source application set limits accurate methodology comparisons. This paper addresses these gaps by providing an open-source application set with real and artificially injected issues and proposing a framework for guided fuzzing. The solutions are language-agnostic and compatible with various hardware. Finally, we evaluate these methods to assess their impact on vulnerability discovery.
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Paper Nr: 38
Title:

Exhaustive Model Identification on Process Mining
Authors:

Takeharu Mitsuda, Hiroyuki Nakagawa, Haruhiko Kaiya, Hironori Takeuchi, Sinpei Ogata and Tatsuhiro Tsuchiya
Abstract: HeuristicsMiner is a process mining technique, which can construct a process model representing dependency relations of each activity from event logs. HeuristicsMiner is notable for its ability to output a process model that removes noise from the input data by allowing the user to set multiple parameters. However, it is difficult for users to understand the characteristics of each parameter and to identify parameter values that enable them to obtain ideal process models. In this study, we propose a method for identifying all possible process models that can be generated from an input event log in HeuristicsMiner. We extract the conditions under which the dependencies in the input logs are represented in the output model, and then create a process model transition table based on these conditions to identify these models. We applied this method to several large logs and mined process models using the combinations of parameter values obtained, and confirmed that process models were efficiently obtained without excesses or deficiencies.
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Paper Nr: 40
Title:

Is It Professional or Exploratory? Classifying Repositories Through README Analysis
Authors:

Maximilian Auch, Maximilian Balluff, Peter Mandl and Christian Wolff
Abstract: This study introduces a new approach to determine whether GitHub repositories are professional or exploratory by analyzing README.md files. We crawled and manually labeled a dataset that contains over 200 repositories to evaluate various classification methods. We compared state-of-the-art Large Language Models (LLM) against traditional Natural Language Processing (NLP) techniques, including term frequency similarity and word embedding-based nearest-neighbors, using RoBERTa. The results demonstrate the advantages of LLMs on the given classification task. When applying a zero-shot classification without multi-step reasoning, GPT-4o had the overall highest accuracy. The implementation of a few-shot learning showed a mixed result in different models. Llama 3 (70b) achieved 89.5% accuracy when using multi-step reasoning, though such improvements were not consistent across all models. Also, our experiments with word probability threshold filtering showed mixed results. Our findings highlight important considerations regarding the balance between accuracy, processing speed, and operational costs. For time-critical applications, we found that direct prompts without multi-step reasoning provide the most efficient approach, while the model size made a smaller contribution. Overall, README.md content proved sufficient for accurate classification in approximately 70% of cases.
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Paper Nr: 52
Title:

Design Thinking and a Human-Centered Approach to Explore the Potential of Mobile Phone and AI-Enabled Just-in-Time Mental Health Solution for University Students in India
Authors:

Raman Saxena
Abstract: Anxiety, stress and depression are the significant mental health and well-being challenges being faced by the university students, early one in three reporting significant struggles. Academic pressure, family expectations, a competitive environment, social isolation, financial stress and stigma surrounding mental health contribute to this issue. These challenges impacts students’ academic performance and social integration negatively, which further impacts their mental health and well-being. Given high mobile phone usage among youths, smartphones offer a unique, discreet avenue, for mental health support. By leveraging device sensors like accelerometers, gyroscopes, GPS, proximity sensors, and biometric readers (e.g. heart rate, SpO2), a mobile framework can analyze user activity pattens, social interactions, and screen time to detect early signs of mental health concerns, such as stress, anxiety or loneliness. Integrating this data with trained mental health models enhances predictive accuracy, enabling personalized help and therapeutic content like calming music, mind-fulness exercises, or relaxation videos, Notifications, and chat bot conversations as a virtual buddy, tailored to their preferences. The framework uses smartphones as an unobtrusive wellness companion, aiming to prevent mental health deterioration while safeguarding, user privacy, thus empowering students with a personal tool for mental health well-being.
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Paper Nr: 59
Title:

Towards a Blockchain-Based CI/CD Framework to Enhance Security in Cloud Environments
Authors:

Sabbir M. Saleh, Nazim Madhavji and John Steinbacher
Abstract: Security is becoming a pivotal point in cloud platforms. Several divisions, such as business organisations, health care, government, etc., have experienced cyber-attacks on their infrastructures. This research focuses on security issues within Continuous Integration and Deployment (CI/CD) pipelines in a cloud platform as a reaction to recent cyber breaches. This research proposes a blockchain-based solution to enhance CI/CD pipeline security. This research aims to develop a framework that leverages blockchain’s distributed ledger technology and tamper-resistant features to improve CI/CD pipeline security. The goal is to emphasise secure software deployment by integrating threat modelling frameworks and adherence to coding standards. It also aims to employ tools to automate security testing to detect publicly disclosed vulnerabilities and flaws, such as an outdated version of Java Spring Framework, a JavaScript library from an unverified source, or a database library that allows SQL injection attacks in the deployed software through the framework.
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Paper Nr: 90
Title:

LICA-HE: Optimal Lossless Image Compression Algorithm for Color Passport-Photo Compression
Authors:

Taif Ammash, Hamza Gharsellaoui and Leila Ben Ayed
Abstract: Recent interest has been generated in multimodal biometrics technology due to its potential to increase recognition rates by overcoming some of the fundamental limitations of single biometric modalities. In order to reduce the volume of big data while it is being transmitted through communication channels and to store it in data warehouses and archives, it is important to use various data compression algorithms. As a result, numerous compression algorithms that are tailored to handle different types of data have been developed. Since the size and accuracy of images are always changing, the problem of storing and transferring images necessitates a compression technique to minimize their size. Images are employed in many different industries in our daily lives, including social networks, medical diagnosis, remote sensing, and other fields. More work is still required, particularly for lossless picture compression, which is a promising method when essential information loss is prohibited, even though numerous data compression techniques have been created to address these issues. The Lossless Image Compression technique Using a Huffman Encoding technique (LICA-HE) is a novel algorithm that we provide in this research. LICA-HE improves state-of-the-art performance, reduces the bit-depth of the image, is efficient, and is modest in complexity. The algorithm was compared with other solutions and is more efficient at compressing ”lossless” images. Experiments conducted on colored images demonstrate that LICA-HE performs better than current techniques in terms of compression rate. Furthermore, our approach demonstrated notable improvements in execution time, with an average of 1.88 seconds for the compression and decompression procedures. With its promise of increased efficacy and efficiency in image compression technologies, LICA-HE pushes the boundaries of lossless image compression.
Paper Nr: 95
Title:

CyberGuardian 2: Integrating LLMs and Agentic AI Assistants for Securing Distributed Networks
Authors:

Ciprian Paduraru, Catalina Camelia Patilea and Alin Stefanescu
Abstract: Robust cybersecurity measures are essential to protect complex information systems from a variety of cyber threats, which requires sophisticated security solutions. This paper explores the integration of Large Language Models (LLMs) to improve cybersecurity operations within Security Operations Centers (SOCs). The proposed framework has a modular plugin architecture where Agentic AI controls the information flow, in-cludes Retrieval Augmented Generation (RAG), protection methods for human-chatbot interactions and tools for managing tasks such as database interactions, code generation and execution. By utilizing these techniques, the framework aims to streamline the workflows of SOC analysts, allowing them to focus on critical tasks rather than redundant activities. The study also explores the dynamic customization of LLMs based on client data, user experience, potential risks and language preferences to ensure a user-centric approach. The results show improvements in efficiency and effectiveness and highlight the potential of LLMs in cybersecu-rity applications.
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Paper Nr: 104
Title:

A User-Centered Design Approach to Develop a Privacy Awareness Application
Authors:

Anika Tabassum Era, Tanjila Kanij, John Grundy and Md Al Mamun
Abstract: There are more than 50 million Facebook users in Bangladesh. Despite its wide usage, Facebook has resulted in negative impacts on individuals and the community, mostly due to improper use of the media. In our prior research, from a survey of almost 200 Facebook users in Bangladesh, we found that some Facebook users had (1) diverse demographic characteristics, (2) varied levels of understanding, and (3) reluctance to learn the features needed help to understand the features to use Facebook properly. In this research, we designed a low-fidelity prototype with interactive video tutorials and quizzes to help them improve their awareness of privacy on Facebook, especially with complicated privacy settings. We conducted a detailed usability evaluation and collected feedback from eleven participants and adjusted the prototype based on the received feedback. Once developed the application will be helpful to improve the overall Facebook experience of users in Bangladesh. We also reflect on the experience of the User-Centered Design (UCD) and recommend how UCD can be planned for other similar user groups.
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Paper Nr: 106
Title:

The Impact of Context-Oriented Programming on Declarative UI Design in React
Authors:

Koshi You, Hiroaki Fukuda and Paul Leger
Abstract: Context-Oriented Programming (COP) is a programming paradigm that modularizes context-dependent behavior. COP provides a language mechanism that enables behavior to switch depending on the current context, such as mobile device orientation or user behavior. COP is particularly promising in domains that frequently involve context-dependent behavior, such as mobile applications and IoT systems. Although various COP abstractions and mechanisms are being proposed, applications implemented with COP are mostly small-scale and designed to validate each proposed approach. These cases are often ideal scenarios for the proposed approach, raising concerns about the sufficiency of the effectiveness assessment. To address this, this paper mimics the functionality of a real-world application and implements it using an application framework. By applying COP to the refactoring, we measure the software metrics of the implementation. As a result, we quantitatively identify the impact of the framework abstraction on the code when COP is used.
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Paper Nr: 123
Title:

A Multivocal Mapping Study on Artifact Traceability Complexities in Practice
Authors:

Zaki Pauzi and Andrea Capiluppi
Abstract: Artifact traceability is essential for managing the relationships between artifacts produced during the software development lifecycle, yet achieving effective traceability in practice remains a complex challenge. This study explores the multifaceted nature of traceability in real-world settings, providing actionable insights for researchers, practitioners, and tool developers aiming to enhance traceability practices, improve software quality, and support project success. Drawing from 56 academic papers and 15 grey literature sources, this study synthesises findings from scholarly research, industry reports, practitioner experiences, and expert opinions. Key challenges include the lack of standardised processes and tools, difficulties in maintaining traceability over time, balancing automation with human involvement, and fostering effective stakeholder communication and collaboration. Two critical open challenges emerge: achieving semantic interoperability and managing scalability in complex systems. To address these, we recommend targeted efforts towards standardisation and the development of incremental, adaptive techniques for traceability management.
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Paper Nr: 126
Title:

Key Artefacts in the Initial Phases of IT Project Management: Systematic Mapping Study
Authors:

Oksana Nikiforova, Kristaps Babris, Megija Krista Miļūne, Navyasri Tanguturi and Óscar Pastor
Abstract: Mistakes made during the initial phases of an IT project are often critical as they can have cascading effect that impact every following phase of the project, especially implementation. These mistakes can lead to increased costs, delays and potential project failure. The initial phases of IT project, such as planning, requirements gathering, and design, set the foundation for the entire project defining project objectives, requirements and scope and setting the direction for the entire project. The paper demonstrates the results of the systematic mapping study performed on the definition of the types of artefacts created during IT project management before the implementation, as it lays the foundation for effective project planning, avoiding common pitfalls and ensuring alignment with industry best practices.
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Paper Nr: 137
Title:

Analyzing Deforestation Dynamics in Romania Using Random Forest Algorithm and Google Earth Engine
Authors:

Andrei Varan, Adriana Mihaela Coroiu and Liviu-Mihai Iacob
Abstract: Despite the vital roles that forests play in reducing erosion and filtering out CO2, illegal logging persists globally. Due to deforestation, agricultural practices, and infrastructure development, Romania, a country with an abundance of natural resources and forests, is facing significant deforestation. In this research, we proposed an approach that uses Google Earth Engine, machine learning, and satellite images to overcome this problem. By combining new technologies, the current Landsat 9 deployment enhances Earth Engine's capabilities and enables improved forest monitoring and analysis. The study uses NASA-provided Landsat images, filtered out for Romania’s surface with an applied reducer and machine learning techniques, both being used in the Google Earth Engine editor, to have a better visualization of Romania's deforestation.
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