Effectively Sampling Higher Order Mutants Using Causal Effect

TL;DR

This paper introduces a novel method for efficiently generating higher order mutants in mutation testing by leveraging causal program dependence analysis to select impactful mutation pairs.

Contribution

It proposes a new approach using CPDA to guide the selection of mutation pairs for higher order mutant generation, improving efficiency.

Findings

Uses CPDA to estimate causal effects of code changes

Heuristic-based selection of mutation pairs

Generates impactful higher order mutants efficiently

Abstract

Higher Order Mutation (HOM) has been proposed to avoid equivalent mutants and improve the scalability of mutation testing, but generating useful HOMs remain an expensive search problem on its own. We propose a new approach to generate Strongly Subsuming Higher Order Mutants (SSHOM) using a recently introduced Causal Program Dependence Analysis (CPDA). CPDA itself is based on program mutation, and provides quantitative estimation of how often a change of the value of a program element will cause a value change of another program element. Our SSHOM generation approach chooses pairs of program elements using heuristics based on CPDA analysis, performs First Order Mutation to the chosen pairs, and generates an HOM by combining two FOMs.