Improvement of Equivalent Mutant Detection Using Loop Count Restriction

Software testing is an indispensable part of the soft-ware development process. Mutation analysis is regarded as an effective software testing method. By adopting mutation operators on the original pro-gram, mutation analysis generates mutants to sim-ulate possible bugs, and then it creates a test case suite working on both mutants and the original pro-gram to analyze the difference in the results and find these bugs. Mutation analysis improves the test case suite until it is strong enough to find all possi-ble bugs. If the result of running a mutant is differ-ent from the result of running the original program for at least one test case in the input test case suite, the seeded fault denoted by the mutant is detected. However, in the process of seeding into the fault, there is the equivalent mutant that has exactly the same behavior as the original program generated. These equivalent mutants obstruct the precise as-sessment of the quality of the test case suite. There-fore, it is necessary to remove them. In this article, we improve a previously proposed novel approach to equivalent mutant detection that uses symbolic computation. When we apply symbolic computa-tion to a statement with loop description, the path explosion problem occurs. To overcome this path explosion problem, this article proposes a method that restricts the number of iterations to improve the detection ratio of equivalent mutants