Pedro Delgado-Pérez

Assessment of class mutation operators for C + + with the MuCPP mutation system

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.

GiGAn: evolutionary mutation testing for C++ object-oriented systems

The reduction of the expenses of mutation testing should be based on well-studied cost reduction techniques to avoid biased results. Evolutionary Mutation Testing (EMT) aims at generating a reduced set of mutants by means of an evolutionary 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 explores its performance when applied to C++ object-oriented programs thanks to a newly developed system, GiGAn. The conducted experiments reveal that EMT shows stable behavior in all the case studies, where the best results are obtained 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.

Assessment of C++ object‐oriented mutation operators: A selective mutation approach

Mutation testing is an effective but costly testing technique. Several studies have observed that some mutants can be redundant and therefore removed without affecting its effectiveness. Similarly, some mutants may be more effective than others in guiding the tester on the creation of high‐quality test cases. On the basis of these findings, we present an assessment of C++ class mutation operators by classifying them into 2 rankings: the first ranking sorts the operators on the basis of their degree of redundancy and the second regarding the quality of the tests they help to design. Both rankings are used in a selective mutation study analysing the trade‐off between the reduction achieved and the effectiveness when using a subset of mutants. Experimental results consistently show that leveraging the operators at the top of the 2 rankings, which are different, lead to a significant reduction in the number of mutants with a minimum loss of effectiveness.

Using Evolutionary Mutation Testing to improve the quality of test suites

Mutation testing is a method used to assess and improve the fault detection capability of a test suite by creating faulty versions, called mutants, of the system under test. Evolutionary Mutation Testing (EMT), like selective mutation or mutant sampling, was proposed to reduce the computational cost, which is a major concern when applying mutation testing. This technique implements an evolutionary algorithm to produce a reduced subset of mutants but with a high proportion of mutants that can help the tester derive new test cases (strong mutants). In this paper, we go a step further in estimating the ability of this technique to induce the generation of test cases. Instead of measuring the percentage of strong mutants within the subset of generated mutants, we compute how much the test suite is actually improved thanks to those mutants. In our experiments, we have compared the extent to which EMT and the random selection of mutants help to find missing test cases in C++ object-oriented systems. We can conclude from our results that the percentage of mutants generated with EMT is lower than with the random strategy to obtain a test suite of the same size and that the technique scales better for complex programs.

Search-based mutation testing to improve performance tests

Performance bugs are common and can cause a significant deterioration in the behaviour of a program, leading to costly issues. To detect them and reduce their impact, performance tests are typically applied. However, there is a lack of mechanisms to evaluate the quality of performance tests, causing many of these bugs remain unrevealed. Mutation testing, a fault-based technique to assess and improve test suites, has been successfully studied with functional tests. In this paper, we propose the use of mutation testing together with a search-based strategy (evolutionary algorithm) to find mutants that simulate performance issues. This novel approach contributes to enhance the confidence on performance tests while reducing the cost of mutation testing.

Performance mutation testing: Hypothesis and open questions

Abstract 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.

Search-based mutant selection for efficient test suite improvement: Evaluation and results

Abstract 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.

Using Evolutionary Computation to Improve Mutation Testing

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.