An error-propagation aware method to reduce the software mutation cost using genetic algorithm

• Published in: Data technologies and applications 2021-01, Vol.55 (1), p.118-148
• Main Authors: Mohammad Javad Hosseini, Seyed, Arasteh, Bahman, Isazadeh, Ayaz, Mohsenzadeh, Mehran, Mirzarezaee, Mitra
• Format: Article
• Language: English
• Subjects: Algorithms; Computer Software; Costs; Efficiency; Equivalence; Error analysis; Experiments; Genetic algorithms; Heuristic methods; Heuristics; Mathematics; Mutation; Propagation; Research Problems; Software quality; Software reliability; Source code; Teaching Methods
• ISSN: 2514-9288; 2514-9318
• DOI: 10.1108/DTA-03-2020-0073

PurposeThe purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.Design/methodology/approachIn a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.FindingsIn order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.Originality/valueThe main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.