Dynamic Mutant Subsumption Analysis using LittleDarwin

TL;DR

This paper presents enhancements to the LittleDarwin tool to effectively identify redundant mutants, addressing the impact of redundant mutants on mutation testing accuracy in software testing research.

Contribution

The paper introduces improvements to LittleDarwin for detecting redundant mutants, facilitating more accurate mutation testing analysis.

Findings

Enhanced LittleDarwin detects redundant mutants more effectively

Redundant mutants significantly affect mutation testing accuracy

Improved tool supports better research in mutation testing

Abstract

Many academic studies in the field of software testing rely on mutation testing to use as their comparison criteria. However, recent studies have shown that redundant mutants have a significant effect on the accuracy of their results. One solution to this problem is to use mutant subsumption to detect redundant mutants. Therefore, in order to facilitate research in this field, a mutation testing tool that is capable of detecting redundant mutants is needed. In this paper, we describe how we improved our tool, LittleDarwin, to fulfill this requirement.