Effectively Sampling Higher Order Mutants Using Causal Effect
This addresses the scalability issue in mutation testing for software developers and testers, but appears to be an incremental improvement by applying an existing analysis technique (CPDA) to a known bottleneck.
The paper tackles the expensive search problem of generating useful Higher Order Mutants (HOMs) in mutation testing by proposing a new approach that uses Causal Program Dependence Analysis (CPDA) to generate Strongly Subsuming HOMs (SSHOMs). The result is a method that selects program element pairs based on CPDA heuristics, applies First Order Mutation to them, and combines these to create HOMs, though no concrete performance numbers are provided in the 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.