Abstract

Abstract
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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract

Abstract
Mutation testing is an effective testing technique for improving how well a test suite can detect small changes to a program under test. This testing technique is seeing increased industry adoption. This paper aims to study the use of mutation testing in an educational setting and understand students' technical and conceptual challenges in applying mutation testing concepts. We report on two case studies of incorporating mutation testing into software engineering curricula. The Scaffolding Study explores the impact of using different mutation analysis tools directly or indirectly via a uniform interface provided by an educational infrastructure. We observe that scaffolding (indirect tool use) improved the consistency of student performance for those using the same mutation analysis tool on the same code as well as helping students perform more effective mutation testing. The Visualization Study explores the impact of different forms of output of a mutation analysis tool. Specifically, it assesses to what extent visualizations support students in reasoning about mutants and writing tests to detect them. We observe that like scaffolding, visualizations helped students perform more effective mutation testing, with lower-performing students seeing a boost in particular. We further explore challenges around automatic assessment of mutation testing exercises. For example, we observe that even with assignment scaffolding, 18-21% of student submissions required manual modifications to successfully execute.

Abstract
Regression testing is a selective retesting of a system or component to verify that modifications have not induced unintended effects and that the system or component maintains compliance with the specified requirements. However, it can be time-consuming and resource-intensive, especially for large systems. Regression testing selection techniques can help address this issue by selecting a subset of test cases to run. The Change Based technique selects a subset of the existing test cases and executes modified classes. Besides effectively reducing the test suite, this technique may reduce the capability of revealing faults. From this perspective, code smells are known to identify poor design and software quality issues. Some works have explored the association between smells and faults with some promising results. Inspired by these results, we propose combining code change and smell to select regression tests and present eight techniques. Additionally, we developed the Regression Testing Selection Tool (RTST) to automate the selection process using these techniques. We empirically evaluated the approach in Defects4J projects by comparing the techniques' effectiveness with the Change Based and Class Firewall as a baseline. The results show that the Change and Smell Intersection Based technique achieves the highest reduction rate in the test suite size but with less class coverage. On the other hand, Change and Smell Firewall technique achieves the lowest test suite size reduction with the highest fault detection effectiveness test cases, suggesting the combination of smells and changed classes can potentially find more bugs. The Smell Based technique provides a comparable class coverage to the code change and smell approach. Our findings indicate opportunities for improving the efficiency and effectiveness of regression testing and highlight that software quality should be a concern throughout the software evolution.

Abstract