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Research review: Swarm intelligence for test prioritisation


Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application” an article authored by Kohani K. Mohan et al. and published in the 4th volume of the Journal of Soft Computing and Data Mining, reviews the use of swarm optimisation algorithms for software test case prioritisation.

Software test case prioritisation involves ordering test cases so that those with a higher importance; or greater likelihood of finding defects, or any other other criteria are executed first. Importance is determined using features such as code coverage, functionality, fault history and customer usage.

Swarm intelligence algorithms are computational methods inspired by the collective behaviour of social insects, such as bees, birds and ants, to solve optimisation problems. For his article, the effectiveness of the Artificial Bee Colony (ABC) and Ant Colony Optimisation (ACO) algorithms were investigated. The ABC and ACO algorithms are popular swarm optimisation algorithms, developed in 2005 and 1991 respectively by scientists who were inspired by their studies of the behaviour of ants and bees. 

The article begins by giving an overview of the popular algorithms currently employed in test case prioritisation, providing qualitative performance comparisons between each. The authors then provide a detailed explanation of the implementation of the ABC and ACO algorithms for test case prioritisation. 

The algorithms were then compared to an original non-prioritised set of test cases, finding that both algorithms outperformed the baseline. The authors conclude that these results indicate the potential for future work in this field and the scope for the implementation of swarm optimization algorithms for software test case prioritisation applications.