Mehrdad Saadatmand

Software quality assurance is pivotal in safety-critical domains such as railway systems, where failures could have catastrophic consequences. In this context, the train control and management system, which enables communication and control across multiple subsystems (such as doors and information panels) within a modern train, and its software must undergo rigorous validation. Alstom Rail Sweden AB employs a digital twin infrastructure to simulate and validate train control and management system software. While this setup improves system-level testing, root-cause analysis of test failures remains a manual, time-consuming bottleneck. In this study, we explore the potential of large language models to automate root cause analysis by interpreting test execution logs generated during digital twin-based testing. We benchmark nine state-of-the-art large language models: Aion−1.0, DeepSeek R1, DeepSeek V3 0324, Mistral Small 3.1 24B, GPT o3-mini, Gemini 2.5 Pro Experimental, QwQ 32B, Gemini 2.0 Flash Experimental, and Amazon Nova 2 Lite using zero-shot chain-of-thought prompting to assess their ability to reason about fault patterns in real-world industrial test execution logs. The logs were sourced from Alstom’s digital twin-based testing environment and captured complex operational behaviour typical of embedded, safety-critical systems. Our results showed that long-context LLMs tended to achieve higher accuracy than smaller models. Gemini 2.5 Pro Experimental achieved the best performance with 66.7% accuracy and produced coherent step-by-step explanations. However, coherent reasoning did not consistently translate into correct root-cause identification, suggesting that LLMs are currently better suited to support human-in-the-loop root cause analysis than to fully automate it, and motivating further work to improve prediction accuracy for log-based RCA.

Context. Software testing is a critical aspect of the software development lifecycle, yet it remains underrepresented in academic curricula. Despite advances in pedagogical practices and increased attention from the academic community, challenges persist in effectively teaching software testing. Understanding these challenges from the teachers’ perspective is crucial to aligning education with industry needs. Objective. To analyze the characteristics, practices, tools, and challenges of software testing courses in higher education, from the perspective of educators, and to assess the integration of recent pedagogical approaches in software testing education. Method. A structured survey consisting of 52 questions was distributed to 143 software testing educators across Western European universities, resulting in 49 valid responses. The survey explored topics taught, course organization, teaching practices, tools and materials used, gamification approaches, and teacher satisfaction. Results. The survey revealed significant variability in course content, structure, and teaching methods. Most dedicated software testing courses are offered at the master’s level and are elective, whereas testing is introduced earlier in less specialized (NST) courses. There is low adoption of formal guidelines (e.g., ACM, SWEBOK), limited integration of non-functional testing types, and a high diversity in textbooks and tools used. While modern practices like Test-Driven Development and automated assessment are increasingly adopted, gamification and active learning approaches remain underutilized. Teachers expressed a need for improved and more consistent teaching materials. Conclusion. The study highlights a mismatch between academic practices and industry expectations in software testing education. Greater integration of standardized curricula, broader adoption of modern teaching tools, and increased support for teachers through high-quality, adaptable teaching materials are needed to enhance the effectiveness of software testing education.

The evolving landscape of software development demands that software testers continuously adapt to new tools, practices, and acquire new skills. This study investigates software testing competency needs in industry, identifies knowledge gaps in current testing education, and highlights competencies and gaps not addressed in academic literature. This is done by conducting two focus group sessions and interviews with professionals across diverse domains, including railway industry, healthcare, and software consulting and performing a curated small-scale scoping review. The study instrument, co-designed by members of the ENACTEST project consortium, was developed collaboratively and refined through multiple iterations to ensure comprehensive coverage of industry needs and educational gaps. In particular, by performing a thematic qualitative analysis, we report our findings and observations regarding: professional training methods, challenges in offering training in industry, different ways of evaluating the quality of training, identified knowledge gaps with respect to academic education and industry needs, future needs and trends in testing education, and knowledge transfer methods within companies. Finally, the scoping review results confirm knowledge gaps in areas such as AI testing, security testing and soft skills.

This paper presents a systematic mapping study classifying existing scientific contributions on synergies of Model Driven Engineering (MDE), Artificial Intelligence/Machine Learning (AI/ML), and DevOps, with the overall objective of supporting the continuous development of Cyber-Physical Systems (CPSs). We collected papers from bibliographic sources and selected primary studies to analyse. Then, we characterised and classified the current state of the art, focusing on 1) main aspects already tackled at the intersection of at least two of the three studied areas, and 2) findings emerging from the analysis as a framework for potential future research, notably regarding the integration of the three studied areas. The results reveal that few approaches combine MDE, AI/ML, and DevOps for software and systems engineering. In contrast, several approaches have combined two of them, specifically MDE and DevOps. Approaches combining AI/ML with MDE or DevOps are also becoming more frequent and will most likely continue to progress in the future. These synergies cover a range of engineering activities, from requirements and design to monitoring, maintenance, and evolution. Open research challenges include advancing AI/ML, MDE, and DevOps integration, supporting scalable, data-oriented solutions, proposing new continuous engineering methods, and adapting DevOps practices to diverse systems.