Using Constraints for Equivalent Mutant Detection

TL;DR

This paper presents a novel constraint-based method for detecting equivalent mutants in mutation testing by identifying test cases that differentiate the program from its mutant, supported by algorithms and initial empirical results.

Contribution

It introduces a new approach using constraint representations to solve the equivalent mutant problem, including algorithms and preliminary empirical validation.

Findings

Effective in distinguishing non-equivalent mutants

Provides a formal constraint-based framework

Initial empirical results show promise

Abstract

In mutation testing the question whether a mutant is equivalent to its program is important in order to compute the correct mutation score. Unfortunately, answering this question is not always possible and can hardly be obtained just by having a look at the program's structure. In this paper we introduce a method for solving the equivalent mutant problem using a constraint representation of the program and its mutant. In particularly the approach is based on distinguishing test cases, i.e., test inputs that force the program and its mutant to behave in a different way. Beside the foundations of the approach, in this paper we also present the algorithms and first empirical results.