Technical Debt - From Financial Metaphor to Daily Practice?
Paris Avgeriou, University of Groningen, Netherlands
High-level Verification and Validation of Software Supporting Business Processes
Hermann Kaindl, TU Wien, Univ. for Continuing Education Krems, Vienna Univ. of Economics and Business, Austria
Model Me If You Can - Challenges and Benefits of Individual and Mass Data Analysis for Enterprises
Marco Brambilla, Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Italy
Technical Debt - From Financial Metaphor to Daily Practice?
Paris Avgeriou
University of Groningen
Netherlands
Brief Bio
Dr. Paris Avgeriou is Professor of Software Engineering at the University of Groningen, the Netherlands where he has led the Software Engineering research group since September 2006. Before joining Groningen, he was a post-doctoral Fellow of the European Research Consortium for Informatics and Mathematics. He is the Editor in Chief of the Journal of Systems and Software, as well as an Associate Editor for IEEE Software. He also sits on the editorial board of Springer Transactions on Pattern Languages of Programming (TPLOP). He has co-organized several international conferences and workshops (mainly at ICSE). His research interests lie in the area of software architecture, with strong emphasis on architecture modeling, knowledge, evolution, patterns and technical debt. He champions the evidence-based paradigm in Software Engineering research and works towards closing the gap between industry and academia.
Abstract
The term Technical Debt has seen widespread adoption in industry and increased interest in academia over the past years. It expresses quality compromises that can yield short-term benefits but may hurt the long-term evolution of a software system. Technical Debt as a metaphor resonates well with technical and non-technical stakeholders and can potentially act as a communication bridge between them, particularly in making internal software qualities explicit. However, a perfect storm is brewing: Technical Debt is dangerously accumulating in most, if not all, large systems, threatening to “bankrupt” those systems if it is not actively managed. In this talk we revisit the state of the art and practice to examine what theories, tools and methods are offered to help manage Technical Debt. We also identify the challenges that lead to accumulation of technical debt or inability to repay it. Finally, we discuss some promising future directions in the field, concluding with a “call to arms”.
High-level Verification and Validation of Software Supporting Business Processes
Hermann Kaindl
TU Wien, Univ. for Continuing Education Krems, Vienna Univ. of Economics and Business
Austria
Brief Bio
Hermann Kaindl joined the Institute of Computer Technology at TU Wien in early 2003 as a full professor, where he served in this position until September 2022, for several years as the department head and the head of the organizational unit entitled “Software-intensive Systems”. He served for several years as a member of the Senate at TU Wien, from October 2019 until September 2022 as a Vice Chairman. After his retirement, Hermann Kaindl is still working on (funded) research projects at three different universities. Prior to moving to academia, he was a senior consultant with the division of program and systems engineering at Siemens AG Austria. There he has gained more than 24 years of industrial experience in requirements and software engineering, human-computer interaction and artificial intelligence. He is a Senior Member of the IEEE and a Distinguished Scientist member of the ACM.
Abstract
High-level Verification and Validation (V&V) of software supporting business processes can be done on the level of a Business Process Model (BPM), since V&V of BPMs indirectly includes an important part of V&V of the software implementing such BPMs (e.g., through service composition). If the BPM is built ‘right’ according to given properties, the software implementing it ‘right’ also satisfies these properties, and if the BPM specifies the ‘right’ process, it is also the ‘right’ software. Semantic specification of services based on formal logic can be used for automated verification of (software) service composition. In order to make such verifications consistent with validations of service compositions in the context of business processes, more and more knowledge needs to be included in the related specifications. Independently of the formalism used, a key challenge is to consistently formalize the process and its properties. While formal verification of business process models (BPMs) can be done through model checking (also known as property checking), formalizing corresponding properties having the process model available may negatively influence the formulation of properties to be checked. In addition, properties should be checkable for several processes. So, we address the problem of formalizing properties without knowing the process model. The deeper issue is that task- and artefact-centric BPMs are mostly used in isolation.
In this context, we developed a new and systematic approach for connecting a task-centric BPM (in BPMN) with a model of an artefact-centric object life cycle through semantic task specification. This allows the formulation of properties for model checking referring to additional models of object life cycles, which together can represent certain business rules. Hence, a combination of conventional business process models (given, e.g., in BPMN), models of business object life cycles, and formalized business rules can be used for verification through model checking. So, we present a seamless approach for formal and automated verification of BPMs using model checking, and a comprehensive approach to V&V of (software) service design.
Model Me If You Can - Challenges and Benefits of Individual and Mass Data Analysis for Enterprises
Marco Brambilla
Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano
Italy
http://dbgroup.como.polimi.it/brambilla/
Brief Bio
Marco Brambilla is associate professor at Politecnico di Milano. He is active in research and innovation, both at industrial and academic level. His research interests include data science, software modeling languages and design patterns, crowdsourcing, social media monitoring, and big data analysis. He has been visiting researcher at CISCO, San Josè, and University of California, San Diego. He has been visiting professor at Dauphine University, Paris.
He is founder of the startup Fluxedo, focusing on social media analysis and Social engagement, and of the company WebRatio, devoted to software modeling tools for Web, Mobile and Business Process based software applications. He is author of various international books and research articles in journals and conferences, with over 200 papers. He was awarded various best paper prizes and gave keynotes and speeches at many conferences and organisations. He is the main author of the OMG standard IFML. He participated in several European and international research projects. He has been reviewer of FP7 projects and evaluator of EU FP7 proposals, as well as of national and local government funding programmes throughout Europe. He has been PC chair of ICWE 2008, Berlin. He is PC member of several conferences and workshops, he organized several workshops and conference tracks so far, and he has been reviewer for many scientific journals. He is associate editor of SIGMOD Records.
Abstract
The current hype on big data, machine learning, and artificial intelligence is starting to affect even the most traditional enterprises and sectors.
In this context, we will explore the potential of large scale data collection and analysis in the area of customer/citizen monitoring, spanning techniques such as crowdsourcing, data fusion, descriptive and predictive analysis, for reconstructing people behaviour and profiling.
The keynote will also consider the perspective of the customer within the context of modern media, where sharing and interactions through digital channels apparently becomes compulsory. Citizens become ready to give up a lot of their information for (marginal?) benefits coming from technology providers that in exchange grab their data.
Through a set of real-world case studies and examples, we will explore techniques, benefits and risks that companies face in approaching user data analysis.