Open AccessMS‐guided many‐objective evolutionary optimisation for test suite minimisation
Author(s) -
Zheng Wei,
Wu Xiaoxue,
Cao Shichao,
Lin Jun
Publication year - 2018
Publication title -
iet software
Language(s) - English
Resource type - Journals
ISSN - 1751-8814
DOI - 10.1049/iet-sen.2018.5133
Subject(s) - test suite , minimisation (clinical trials) , computer science , suite , sorting , evolutionary algorithm , test case , software , reliability engineering , machine learning , algorithm , statistics , mathematics , engineering , programming language , archaeology , history , regression analysis
Test suite minimisation is a process that seeks to identify and then eliminate the obsolete orredundant test cases from the test suite. It is a trade‐off between cost andother value criteria and is appropriate to be described as a many‐objectiveoptimisation problem. This study introduces a mutation score (MS)‐guidedmany‐objective optimisation approach, which prioritises the fault detectionability of test cases and takes MS, cost and three standard code coveragecriteria as objectives for the test suite minimisation process. They use sixclassical evolutionary many‐objective optimisation algorithms to identifyefficient test suite, and select three small programs from the Software‐ArtefactInfrastructure Repository (SIR) and two larger program space and gzip forexperimental evaluation as well as statistical analysis. The experiment resultsof the three small programs show non‐dominated sorting genetic algorithm II(NSGA‐II) with tuning was the most effective approach. However, MOEA/D‐PBI andMOEA/D‐WS outperform NSGA‐II in the cases of two large programs. On the otherhand, the test cost of the optimal test suite obtained by their proposedMS‐guided many‐objective optimisation approach is much lower than the onewithout it in most situation for both small programs and large programs.