Mutation reduction in software mutation testing using firefly optimization algorithm

Abstract

PurposeFor delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study.Design/methodology/approachWith regard to the 80–20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program; then, the mutation operators are injected only on the identified fault-prone instructions.FindingsThe source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test.Originality/valueThe proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In 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. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm as a heuristic algorithm is used for identifying the most error-prone paths of a program; then, the mutation operators (faults) are injected only on the identified fault-prone instructions.

An Argentine ant system algorithm for partial set covering problem

“…In this way, the heuristic algorithm becomes a good choice. Although a large number of heuristic methods have been proposed to solve various optimization problems (Zhou et al, 2018(Zhou et al, , 2020Arasteh et al, 2020;Shomali and Arasteh, 2020), as far as we know, no heuristic algorithms exist to solve the PSCP. Therefore, the purpose of this work is to enrich the repertory of solution methods for PSCP by presenting a novel Argentine ant system (AAS) algorithm.…”

ImReMuDF: Redundant Mutants Identification Method Based on Definition and Reference of Variables

“…Chekam et al [13,14] proposed a dynamic symbolic execution method and a mutant priority method. Shomali and Arasteh [15] proposed the firefly optimization algorithm as a heuristic algorithm for identifying the most error-prone path in the program. Hooseini et al [16] proposed a genetic algorithm to identify the path where the program is most likely to propagate errors as the mutation location.…”