Context: Mutation testing has been mainly analyzed regarding traditional mutation operators involving structured programming constructs common in mainstream languages, but mutations at the class level have not been assessed to the same extent. This fact is noteworthy in the case of C + + , despite being one of the most relevant languages including object-oriented features. Objective: This paper provides a complete evaluation of class operators for the C + + programming language. MuCPP, a new system devoted to the application of mutation testing to this language, was developed to this end. This mutation system implements class mutation operators in a robust way, dealing with the inherent complexity of the language. Method:MuCPP generates the mutants by traversing the abstract syntax tree of each translation unit with the Clang API, and stores mutants as branches in the Git version control system. The tool is able to detect duplicate mutants, avoid system headers, and drive the compilation process. Then, MuCPP is used to conduct experiments with several open-source C + + programs. Results: The improvement rules listed in this paper to reduce unproductive class mutants have a significant impact in the computational cost of the technique. We also calculate the quantity and distribution of mutants generated with class operators, which generate far fewer mutants than their traditional counterparts. Conclusions: We show that the tests accompanying these programs cannot detect faults related to particular object-oriented features of C + + . In order to increase the mutation score, we create new test scenarios to kill the surviving class mutants for all the applications. The results confirm that, while traditional mutation operators are still needed, class operators can complement them and help testers further improve the test suite.

Automated test case generation has proven to be useful to reduce the usually high expenses of software testing. However, several studies have also noted the skepticism of testers regarding the comprehension of generated test suites when compared to manually designed ones. This fact suggests that involving testers in the test generation process could be helpful to increase their acceptance of automatically-produced test suites. In this paper, we propose incorporating interactive readability assessments made by a tester into EvoSuite, a widely-known evolutionary test generation tool. Our approach, InterEvo-TR , interacts with the tester at different moments during the search and shows different test cases covering the same coverage target for their subjective evaluation. The design of such an interactive approach involves a schedule of interaction, a method to diversify the selected targets, a plan to save and handle the readability values, and some mechanisms to customize the level of engagement in the revision, among other aspects. To analyze the potential and practicability of our proposal, we conduct a controlled experiment in which 39 participants, including academics, professional developers, and student collaborators, interact with InterEvo-TR . Our results show that the strategy to select and present intermediate results is effective for the purpose of readability assessment. Furthermore, the participants' actions and responses to a questionnaire allowed us to analyze the aspects influencing test code readability and the benefits and limitations of an interactive approach in the context of test case generation, paving the way for future developments based on interactivity.

Mutation testing exploits artificial faults to measure the adequacy of test suites and guide their improvement. It has become an extremely popular testing technique as evidenced by the vast literature, numerous tools, and research events on the topic. Previous survey papers have successfully compiled the state of research, its evolution, problems, and challenges. However, the use of mutation testing in practice is still largely unexplored. In this paper, we report the results of a thorough study on the use of mutation testing in GitHub projects. Specifically, we first performed a search for mutation testing tools, 127 in total, and we automatically searched the GitHub repositories including evidence of their use. Then, we focused on the top ten most widely used tools, based on the previous results, and manually revised and classified over 3.5K GitHub active repositories importing them. Among other findings, we observed a recent upturn in interest and activity, with Infection (PHP), PIT (Java) and Humbug (PHP) being the most widely used mutation tools in recent years. The predominant use of mutation testing is development, followed by teaching and learning, and research projects, although with significant differences among mutation tools found in the literature—less adopted and largely used in teaching and research—and those found in GitHub only—more popular and more widely used in development. Our work provides a new and encouraging perspective on the state of practice of mutation testing.

For software quality assurance, many safety-critical industries appeal to the use of dynamic testing and structural coverage criteria. However, there are reasons to doubt the adequacy of such practices. Mutation testing has been suggested as an alternative or complementary approach but its cost has traditionally hindered its adoption by industry, and there are limited studies applying it to real safety-critical code. This paper evaluates the effectiveness of state-of-the-art mutation testing on safety-critical code from within the U.K. nuclear industry, in terms of revealing flaws in test suites that already meet the structural coverage criteria recommended by relevant safety standards. It also assesses the practical feasibility of implementing such mutation testing in a real setting. We applied a conventional selective mutation approach to a C codebase supplied by a nuclear industry partner and measured the mutation score achieved by the existing test suite. We repeated the experiment using trivial compiler equivalence (TCE) to assess the benefit that it might provide. Using a conventional approach, it first appeared that the existing test suite only killed 82% of the mutants, but applying TCE revealed that it killed 92%. The difference was due to equivalent or duplicate mutants that TCE eliminated. We then added new tests to kill all the surviving mutants, increasing the test suite size by 18% in the process. In conclusion, mutation testing can potentially improve fault detection compared to structural-coverage-guided testing, and may be affordable in a nuclear industry context. The industry feedback on our results was positive, although further evidence is needed from application of mutation testing to software with known real faults.