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Testing is a crucial activity in the development of software systems. With the increasing complexity of software projects, the industry requires incorporating graduates with adequate testing skills and preparation in this field. A challenge in software testing education is to make students perceive the benefits of writing tests and assess their quality with advanced testing techniques. In this paper, we present an experience integrating both mutation testing and self/peer assessment –two of the most used techniques to that end in the past– into a software testing course during three years. This experience allowed us to analyze the effect of applying these strategies on the students’ perception of their manually-written test suites. Noticeably, the computation of the mutation score significantly undermined the initial expectations they had on the developed test suites. Also, the application of peer testing helped them estimate the relative quality of two comparable test suites, as we found a notable correspondence with their respective mutation coverage. Besides, a more in-depth analysis revealed that the students' test suites with more test cases did not always achieve the highest scores, that they found more readable their own tests, and that they tended to cover the basic operations while forgetting about more advanced features. An opinion survey confirmed the impact that the use of mutants had on their perception about testing, and they mostly supported paying a higher level of attention to testing concepts in software engineering degree plans., The work was partially funded by the European Commission (FEDER), the Spanish Ministry of Science, Innovation and Universities under the project FAME (RTI2018-093608-B-C33), the European project ASSETs (612678-EPP-1-2019-1-IT-EPPKA2-SSA-B), and the University of Cádiz.

Test generation is a costly but necessary testing activity to increase the quality of software projects. Automated testing tools based on evolutionary computation principles constitute an appealing modern approach to support testing tasks. However, these tools still find difficulties to detect certain types of plausible faults in real-world projects. Besides, recent studies have shown that, in general, automatically-generated tests do not resemble those manually written and, consequently, testers are reluctant to adopt them. We observe two key issues, namely the opacity of the process and the lack of cooperation with the tester, currently hampering the acceptance of automated results. Based on these findings, we explore in this paper how the interaction between current tools and expert testers would help address the test case generation problem. More specifically, we identify a number of interaction opportunities related to the object-oriented test case design driven to boost their readability and detection power. Using EvoSuite as base implementation, we present a proof of concept focused on the possibility to integrate readability assessment of the most promising test suites into a genetic algorithm., Work partially funded by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33 and RED2018-102472-T], the University of Cordoba (Plan Propio - mod. 2.4), and the University of Malaga

Industry 4.0 is changing every perspective of production. This new industry requires the evolution of every aspect of the systems faster than ever before. In this situation, validation and verification emerge as a significant phase of the life cycle of software projects. A software failure could result in catastrophic consequences, so it is crucial that all systems work correctly in production. In this work, we present a collaborative project between the University of Cadiz and Navantia. Navantia is a leader shipbuilding Spanish company which develops grand industrial software projects. The objective of this work is to decrease the total cost of these projects by automatizing the software testing phase.

Context: Search-based techniques have been applied to almost all areas in software engineering, especially to software testing, seeking to solve hard optimization problems. However, the problem of selecting mutants to improve the test suite at a lower cost has not been explored to the same extent as other problems, such as mutant selection for test suite evaluation or test data generation. Objective: In this paper, we apply search-based mutant selection to enhance the quality of test suites efficiently. Namely, we use the technique known as Evolutionary Mutation Testing (EMT), which allows reducing the number of mutants while preserving the power to refine the test suite. Despite reported benefits of its application, the existing empirical results were derived from a limited number of case studies, a particular set of mutation operators and a vague measure, which currently makes it difficult to determine the real performance of this technique. Method: This paper addresses the shortcomings of previous studies, providing a new methodology to evaluate EMT on the basis of the actual improvement of the test suite achieved by using the evolutionary strategy. We make use of that methodology in new experiments with a carefully selected set of real-world C++ case studies. Results: EMT shows a good performance for most case studies and levels of demand of test suite improvement (around 45% less mutants than random selection in the best case). The results reveal that even a reduced subset of mutants selected with EMT can serve to increase confidence in the test suite, especially in programs with a large set of mutants. Conclusions: These results support the use of search-based techniques to solve the problem of mutant selection for a more efficient test suite refinement. Additionally, we identify some aspects that could foreseeably help enhance EMT.

Performance bugs are common, costly, and elusive. Performance tests aim to detect performance bugs by running the program with specific inputs and determining whether the observed behaviour is acceptable. There not exist mechanisms, however, to assess the effectiveness of performance tests. Mutation testing is a technique to evaluate and enhance functional test suites by seeding artificial faults in the program under test. In this new idea paper, we explore the applicability of mutation testing to assess and improve performance tests. This novel approach is motivated with examples and open questions.

The reduction of the expenses of mutation testing should be based on well-studied cost reduction techniques to avoid bi ased results. Evolutionary Mutation Testing (EMT) aims at generating a reduced set of mutants by means of an evolu tionary algorithm, which searches for potentially equivalent and difficult to kill mutants to help improve the test suite. However, there is little evidence of its applicability to other contexts beyond WS-BPEL compositions. This study ex plores its performance when applied to C++ object-oriented programs thanks to a newly developed system, GiGAn. The conducted experiments reveal that EMT shows stable be havior in all the case studies, where the best results are ob tained when a low percentage of the mutants is generated. They also support previous studies of EMT when compared to random mutant selection, reinforcing its use for the goal of improving the fault detection capability of the test suite. Universidad de Cádiz PU-EPIF-FPI-PPI-BC 2012-037 Ministerio de Economía y Competitividad TIN2015-65845-C3-3-R (DarDos) Ministerio de Economía y Competitividad TIN2015-70560-R (BELI) Ministerio de Ciencia, Innovación y Universidades TIN2015-71841-REDT (SEBASEnet) Junta de Andalucía P12-TIC-1867 (COPAS)

The work on mutation testing has attracted a lot of attention during the last decades. Mutation testing is a powerful mechanism to improve the quality of test suites based on the injection of syntactic changes into the code of the original program. Several studies have focused on reducing the high computational cost of applying this technique and increasing its efficiency. Only some of them have tried to do it through the application of genetic algorithms. Genetic algorithms can guide through the generation of a reduced subset of mutants without significant loss of information. In this paper, we analyse recent advances in mutation testing that contribute to reduce the cost associated to this technique and propose to apply them for addressing current drawbacks in Evolutionary Mutation Testing (EMT), a genetic algorithm based technique with promising experimental results so far.

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.