Abstract
Regression testing is a software engineering maintenance activity that involves re-executing test cases on a modified software system to check whether code changes introduce new faults. 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 test cases based on the modified software classes, reducing the test suite size. Thereby, it will cover a smaller number of classes, decreasing the efficiency of the test suite to reveal design flaws. From this perspective, code smells are known to identify poor design and threaten the quality of software systems. In this study, we propose an approach to combine code change and smell to select regression tests and present two new techniques: code smell based and code change and smell. Additionally, we developed the Regression Testing Selection Tool (RTST) to automate the selection process. We empirically evaluated the approach in Defects4J projects by comparing the new techniques' effectiveness with the change-based as a baseline. The results show that the change-based technique achieves the highest reduction rate in the test suite size but with less class coverage. On the other hand, test cases selected using code smells and changed classes combined can potentially find more bugs. The code smell-based technique provides a comparable class coverage to the code change and smell approach. Our findings highlight the benefits of incorporating code smells in regression testing selection and suggest opportunities for improving the efficiency and effectiveness of regression testing.

Abstract
Software testing is an important part of the software development lifecycle. As it is a highly sought-after skill in the industry, it is not surprising that there has been a great deal of research into the teaching of software testing in higher education. Most of this research proposes or evaluates pedagogical approaches or software testing tools to assist teachers in educating the next generation of software engineers. These evaluations are often limited to measuring teachers' opinions about the use of a novel pedagogical approach or an educational tool and students' acceptance and performance in terms of desired software testing skills. While tools and pedagogical approaches address specific aspects of a course, to date, little attention has been paid to the opinions of the students about all the individual aspects of a software testing course. This paper aims to address this missing student perspective by taking a holistic view of software testing course designs. To address this gap, an exploratory study was performed by distributing a questionnaire to 103 students from ten different courses to gauge their opinions on a software testing course they are enrolled in. The results show that students generally have a positive perception of the different aspects of their software testing course. However, several areas for improvement were suggested based on the gathered data.

Abstract
Platforms to support novices learning to program are often accompanied by automated next-step hints that guide them towards correct solutions. Many of those approaches are data-driven, building on historical data to generate higher quality hints. Formal specifications are increasingly relevant in software engineering activities, but very little support exists to help novices while learning. Alloy is a formal specification language often used in courses on formal software development methods, and a platform—Alloy4Fun—has been proposed to support autonomous learning. While non-data-driven specification repair techniques have been proposed for Alloy that could be leveraged to generate next-step hints, no data-driven hint generation approach has been proposed so far. This paper presents the first data-driven hint generation technique for Alloy and its implementation as an extension to Alloy4Fun, being based on the data collected by that platform. This historical data is processed into graphs that capture past students’ progress while solving specification challenges. Hint generation can be customized with policies that take into consideration diverse factors, such as the popularity of paths in those graphs successfully traversed by previous students. Our evaluation shows that the performance of this new technique is competitive with non-data-driven repair techniques. To assess the quality of the hints, and help select the most appropriate hint generation policy, we conducted a survey with experienced Alloy instructors. © The Author(s) 2025.

Abstract
Mutation testing has evolved beyond academic research, is deployed in industrial and open-source settings, and is increasingly part of universities' software engineering curricula. While many mutation testing tools exist, each with different strengths and weaknesses, integrating them into educational activities and exercises remains challenging due to the tools' complexity and the need to integrate them into a development environment. Additionally, it may be desirable to use different tools so that students can explore differences, e.g., in the types or numbers of generated mutants. Asking students to install and learn multiple tools would only compound technical complexity and likely result in unwanted differences in how and what students learn. This paper presents FRAFOL, a framework for learning mutation testing. FRAFOL provides a common environment for using different mutation testing tools in an educational setting. © 2024 Owner/Author.

Abstract
Context: Testing activities are essential for the quality assurance of mobile applications under development. Despite its importance, some studies show that testing is not widely applied in mobile applications. Some characteristics of mobile devices and a varied market of mobile devices with different operating system versions lead to a highly fragmented mobile ecosystem. Thus, researchers put some effort into proposing different solutions to optimize mobile application testing. Objective: The main goal of this paper is to provide a categorization and classification of existing testing infrastructures to support mobile application testing. Methods: To this aim, the study provides a Systematic Mapping Study of 27 existing primary studies. Results: We present a new classification and categorization of existing types of testing infrastructure, the types of supported devices and operating systems, whether the testing infrastructure is available for usage or experimentation, and supported testing types and applications. Conclusion: Our findings show a need for mobile testing infrastructures that support multiple phases of the testing process. Moreover, we showed a need for testing infrastructure for context-aware applications and support for both emulators and real devices. Finally, we pinpoint the need to make the research available to the community whenever possible.