Parallel Execution of Programs as a Support for Mutation Testing: A Replication Study

Abstract

Mutation testing is well known as one of the most effective approaches to create test cases, which can detect software faults. However, its drawback is the low scalability — if no special attention is given to improve efficiency — that directly affects its application in practice. This paper shows a replication study focused on emphasizing evidence in which the use of distributed processing structures can improve mutation testing. For this purpose, an architecture that enables mutation testing concurrent execution was designed. Five load balancing algorithms responsible for controlling the distribution and execution of data while carrying out mutation testing were evaluated. Experiments were conducted in order to evaluate the scalability and performance of the architecture considering homogeneous and heterogeneous setups. A time reduction of 50% was observed when executing mutants in parallel in relation to the conventional sequential application of mutation testing. The performance gain was above 95% when there was a higher number of nodes in the distributed architecture.