MDI4SE 2021 Abstracts

Area 1 - Model-Driven Innovations for Software Engineering
Full Papers Short Papers

Area 1 - Model-Driven Innovations for Software Engineering

Full Papers
Paper Nr: 2
Title:

Text Processing Techniques in Approaches for Automated Composition of Domain Models
Authors:

Viktorija Gribermane and Erika Nazaruka
Abstract: Text processing techniques are critical for automated analysis of domain documentation. Proper domain analysis may include analysis of a huge number of documents that may describe business procedures, policies, organizational structures, regulations, etc., as well as minutes of discussions with domain experts. The results of analysis are also presented as documents with or without supporting graphics, e.g., domain models. Automated composition of domain models (including software models) should decrease the time necessary for analysis and would provide traceability to the original documentation. The goal of this research is to understand how text processing techniques can be applied for composition of those models and what are the current trends in this field. The result of analysis of 15 approaches showed that Natural Language Processing features are just the starting point in document processing. The main difficulty is proper analysis of dependencies among words in sentences and among sentences themselves. The obtained results indicate two directions in identification of patterns of those dependencies and a complete diversity in their applications.
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Paper Nr: 5
Title:

Agile Enterprise Architecture by Leveraging Use Cases
Authors:

Hong Guo, Jingyue Li, Shang Gao and Darja Smite
Abstract: Despite benefits Enterprise Architecture (EA) has brought, EA has also been challenged due to its complexity, heavy workload demands, and poor user acceptance. Researchers and practitioners proposed to use EA in an agile and "business outcome-driven" way. This means that EA should not primarily be developed and used according to a pre-defined framework. Instead, EA should be developed and used for specific business purposes and by means of concrete deliverables. By doing so, a more effective and efficient way of EA application could be enabled. However, there is no common agreement on what types of business goals can be expected to be achieved by using EA (The What) and how to achieve these goals through EA solutions (The How). To address these issues, we analysed the information provided by leading EA tool vendors available on their websites to get inspiration. The results showed that Use Cases (UCs) are used generally to motivate potential EA users by focusing on specific business issues. Then, EA solutions to address such business requirements or challenges are scoped and derived accordingly. We expect relevant findings could bring inspiration to agile EA engineering, change the EA “heavy-weight” reputation, and improve the application of EA even among its sceptics.
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Short Papers
Paper Nr: 4
Title:

Overview of Model Interpretation Techniques and Their Applications
Authors:

Vladislavs Nazaruks and Jānis Osis
Abstract: Model interpretation opens quite wide opportunities for domain analysts and software developers. The main weakness related to using domain models in software development is a lack of resources in projects and an invisible value for business stakeholders. Certainly, the doubts that model interpretation will provide additional resources for projects are objective, but it could allow dealing with the existing ones by increasing the value of domain modeling and analysis to the business stakeholders. Two main factors can raise this probability: first, visible execution of models or at least helpful results of this, and second, the opportunity to modify the model and see how changes affect the behavior of the planned system. This paper overviews existing model interpretation techniques with the aim to understand possible architectural solutions, their advantages and weaknesses for certain models. The results of this overview can help researchers those of starting development of their own model interpreter.
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Paper Nr: 6
Title:

Property Lifecycle Diagram for Tracing State Machine Diagram Changes
Authors:

Shinpei Ogata, Yusuke Nishizawa, Erina Makihara, Mizue Kayama and Kozo Okano
Abstract: For geographically distributed systems such as IoT (Internet of Things) and CPS (Cyber-Physical System), those systems provide numerous different components. Furthermore, a lot of those components including future ones must need to interact with each other. Hence, they are designed by event-driven manners for keeping highly versatility. Meanwhile, the behavioral design of such a component is changed by changing the behavioral design of other components. Such changes thus occur frequently depending on the performance, location, etc. of those components. Therefore, diagram changes should be traceable. This paper proposes a property lifecycle diagram and a method to generate it from the edit log of a state machine model. The property lifecycle diagram visualizes the lifecycle of property values for enabling developers to intuitively trace the changes in the property values of the same state machine diagram. This study aimed to answer the following research question: “what clues can the lifecycle of properties provide to understand the changes of the diagram?” To achieve this aim, we have evaluated the proposed method by applying it to the edit log by 10 computer science students.
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Paper Nr: 7
Title:

Using MDE for Teaching Database Query Optimizer
Authors:

Abdelkader Ouared and Abdelhafid Chadli
Abstract: Query optimization is considered as an important part of Data Base Management Systems (DBMS) and plays a major role in database research community (e.g. SQL, Spark, Map-reduce). Generally, this optimization is done using Cost Base Optimization (CBO), which is difficult to understand due to the complexity of platform, database, query and DBMS. Moreover, database query optimizer is usually a very complex process, with difficult concepts depending on the behaviour of the query engine of DBMS. Therefore, users (e.g. novice user, developer, DB administrator) have difficulties to understand and build a mental image of database query optimizer in order to produce more efficient queries. In this paper, we propose a Framework based on the model-driven engineering (MDE) paradigm to facilitate understanding and improving query performance. Indeed, MDE approach has been proven useful for developing new software applications, and its adoption for a teaching perspective presents a major challenge. We illustrate our proposal with use case and proofofconcept prototype for the whole provided process.
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