Parallel mutation testing for large scale systems

Pablo C. Cañizares*, Alberto Núñez, Rosa Filgueira, Juan de Lara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Mutation testing is a valuable technique for measuring the quality of test suites in terms of detecting faults. However, one of its main drawbacks is its high computational cost. For this purpose, several approaches have been recently proposed to speed-up the mutation testing process by exploiting computational resources in distributed systems. However, bottlenecks have been detected when those techniques are applied in large-scale systems. This work improves the performance of mutation testing using large-scale systems by proposing a new load distribution algorithm, and parallelising different steps of the process. To demonstrate the benefits of our approach, we report on a thorough empirical evaluation, which analyses and compares our proposal with existing solutions executed in large-scale systems. The results show that our proposal outperforms the state-of-the-art distribution algorithms up to 35% in three different scenarios, reaching a reduction of the execution time of—at best—up to 99.66%.
Original languageEnglish
Pages (from-to)2071–2097
Number of pages27
JournalCluster Computing
Volume27
Early online date20 Jun 2023
DOIs
Publication statusPublished - Apr 2024

Keywords / Materials (for Non-textual outputs)

  • mutation testing
  • parallel mutation testing
  • large scale systems
  • high performance computing
  • distributed systems
  • testing

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