Abstract
Software testing is an indispensable component of software development, yet it often receives insufficient attention. The lack of a robust testing culture within computer science and informatics curricula contributes to a shortage of testing expertise in the software industry. Addressing this problem at its root -education- is paramount. In this paper, we conduct a comprehensive mapping review of software testing courses, elucidating their core attributes and shedding light on prevalent subjects and instructional methodologies. We mapped 117 courses offered by Computer Science (and related) degrees in 49 academic institutions from four Western European countries, namely Belgium, Italy, Portugal and Spain. The testing subjects were mapped against the conceptual framework provided by the ISO/IEC/IEEE 29119 standard on software testing. Among the results, the study showed that dedicated software testing courses are offered by only 39% of the analysed universities, whereas the basics of software testing are taught in at least one course at every university. The analysis of the software testing topics highlights the gaps that need to be filled in order to better align the current academic offerings with the real industry needs.

Abstract
Context: Mutation testing is a rigorous approach for assessing the quality of test suites by injecting faults (i.e., mutants) into software under test. Tools, such as CosmicRay and Mutpy, are examples of Mutation Testing tools for Python software programs. Problem: With different Python mutation testing tools, comparative analysis is lacking to evaluate their effectiveness in different usage scenarios. Furthermore, the evolution of these tools makes continuous evaluation of their functionalities and characteristics necessary. Method: In this work, we evaluate (statically and dynamically) four Python mutation testing tools, namely CosmicRay, MutPy, MutMut, and Mutatest. In static evaluation, we introduce a comparison framework, adapted from one previously applied to Java tools, and collected information from tool documentation and developer surveys. For dynamic evaluation, we use tests built based on those produced by Pynguin, which are improved through the application of Large Language Models (LLMs) and manual analyses. Then, the adequate test suites were cross-tested among different tools to evaluate their effectiveness in killing mutants each other. Results: Our findings reveal that CosmicRay offers superior functionalities and customization options for mutant generation compared to its counterparts. Although CosmicRay’s performance was slightly lower than MutPy in the dynamic tests, its recent updates and active community support highlight its potential for future enhancements. Cross-examination of the test suites further shows that mutation scores varied narrowly among tools, with a slight emphasis on MutPy as the most effective mutant fault model. © 2024 Copyright held by the owner/author(s).

Abstract
Software testing plays a fundamental role in software engineering, involving the systematic evaluation of software to identify defects, errors, and vulnerabilities from the early stages of the development process. Education in software testing is essential for students and professionals, as it promotes quality and favours the construction of reliable software solutions. However, motivating students to learn software testing may be a challenge. To overcome this, educators may incorporate some strategies into the teaching and learning process, such as real-world examples, interactive learning, and gamification. Gamification aims to make learning software testing more engaging for students by creating a more enjoyable experience. One approach that has proven effective is to use serious games. This paper presents a novel serious game to teach white-box testing test case design techniques, named GAMFLEW (GAMe For LEarning White-box testing). It describes the design, game mechanics, and its implementation. It also presents a preliminary evaluation experiment with students to assess the usability, learnability, and perceived problems, among other aspects. The results obtained are encouraging.

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