Using a swarm to detect hard-to-kill mutants

Abstract

Mutation Testing is an effective testing technique that relies in the generation of mutants from the system under test. The main limitation of this technique is that the potential number of mutants is usually huge. Therefore, it is important to classify and select mutants in order to avoid repetitive, useless or excessive computations, and biased results. In this paper we focus on avoiding too many executions and/or biased results by classifying mutants into two categories: hard-to-kill and easy-to-kill mutants. We propose a new swarm intelligence algorithm to classify a set of mutants between those two classes and we show how our algorithm compares to other approaches.