MS-guided many-objective evolutionary optimisation for test suite minimisation

作者：Zheng, Wei; Wu, Xiaoxue^*; Cao, Shichao; Lin, Jun

来源：IET Software, 2018, 12(6): 547-554.

DOI：10.1049/iet-sen.2018.5133

• statistical analysis
• Pareto optimisation
• minimisation
• genetic algorithms
• program testing
• statistical analysis
• nondominated sorting genetic algorithm II
• program space
• software-artefact infrastructure repository
• MS-guided many-objective optimisation approach
• optimal test suite
• test cost
• NSGA-II
• test suite minimisation process
• standard code coverage criteria
• mutation score-guided many-objective optimisation approach
• many-objective optimisation problem
• redundant test cases

Test suite minimisation is a process that seeks to identify and then eliminate the obsolete or redundant test cases from the test suite. It is a trade-off between cost and other value criteria and is appropriate to be described as a many-objective optimisation problem. This study introduces a mutation score (MS)-guided many-objective optimisation approach, which prioritises the fault detection ability of test cases and takes MS, cost and three standard code coverage criteria as objectives for the test suite minimisation process. They use six classical evolutionary many-objective optimisation algorithms to identify efficient test suite, and select three small programs from the Software-Artefact Infrastructure Repository (SIR) and two larger program space and gzip for experimental evaluation as well as statistical analysis. The experiment results of the three small programs show non-dominated sorting genetic algorithm II (NSGA-II) with tuning was the most effective approach. However, MOEA/D-PBI and MOEA/D-WS outperform NSGA-II in the cases of two large programs. On the other hand, the test cost of the optimal test suite obtained by their proposed MS-guided many-objective optimisation approach is much lower than the one without it in most situation for both small programs and large programs.

• 出版日期2018-12
• 单位西北工业大学