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Thu 15 Jun 2023 16:50 - 17:00 at Aurora Hall - Software Development Processes Chair(s): Eray TüzünIn DevOps, the traceability of software artifacts is critical to the successful development and operation of project delivery to stakeholders. This paper reports on the experience of developers using DevOps for developing and productionising a Javascript React web application, with a focus on traceability management of artifacts produced throughout the life cycle. This report also highlights key opportunities and challenges in traceability management from the development stage to production.
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Wed 14 Jun 2023 16:30 - 16:40 at Aurora Hall - Methodology and Secondary Studies Chair(s): Thomas FehlmannA key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP).
Our search strategy gathered a total of 96 papers in focus of our study, covering a period from 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers, and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability, and data availability.
Based on these, we consolidated the following open challenges: representation similarity across artifacts, the effectiveness of NLP for traceability, and achieving scalable, adaptive, and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability and efforts in achieving interoperability and explainability in NLP models for traceability.
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Wed 14 Jun 2023 16:40 - 16:50 at Aurora Hall - Methodology and Secondary Studies Chair(s): Thomas FehlmannContext: Burnout is a work-related syndrome that, similar to many occupations, influences most software developers. For decades, studies in software engineering(SE) have explored the causes of burnout and its consequences among IT professionals.
Objective: This paper is a systematic mapping study (SMS) of the studies on burnout in SE, exploring its causes and consequences, and how it is studied (e.g., choice of data).
Method: We conducted a systematic mapping study and identified 92 relevant research articles dating as early as the early 1990s, focusing on various aspects and approaches to detect burnout in software developers and IT professionals.
Results: Our study shows that early research on burnout was primarily qualitative, which has steadily moved to more quantitative, data-driven in the last decade.
The emergence of machine learning (ML) approaches to detect burnout in developers has become a de-facto standard.
Conclusion: Our study summarises what we now know about burnout, how software artifacts indicate burnout, and how machine learning can help its early detection. As a comprehensive analysis of past and present research works in the field, we believe this paper can help future research and practice focus on the grand challenges ahead and offer necessary tools.
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