Investigating the performance of SPEA-II on automatic test case generation

Bachelor thesis (2023)

Authors

C.R.E. Li Electrical Engineering, Mathematics and Computer Science

Contributors

A. Panichella Software Engineering - EEMCS (mentor)
Mitchell Olsthoorn Software Engineering - EEMCS (mentor)
D.M. Stallenberg Software Engineering - EEMCS (mentor)
S.E. Verwer Cyber Security - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Erwin Li
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Software testing is an important but time-consuming task, making automatic test case generation an appealing solution. The current state-of-the-art algorithm for test case generation is DynaMOSA, which is an improvement of NSGA-II that applies domain knowledge to make it more suitable for test case generation. Although these enhancements are applicable to other evolutionary algorithms,
no research has been done on how effective other algorithms can function as the base. In this paper, we apply the DynaMOSA modifications to SPEA-II to create a new algorithm, DynaSPEA-II. We conduct an empirical experiment where we evaluate the DynaMOSA enhancements, and directly compare DynaSPEA-II to
DynaMOSA. The algorithms are assessed on a benchmark consisting of 36 diverse JavaScript classes w.r.t. branch coverage. Our results show that adding DynaMOSA enhancements to SPEA-II results in higher coverage in 13.9% of classes, with an average increase of 4.92% for classes where a statistically significant difference was found. DynaSPEA-II performed equally to DynaMOSA, with no statistically significant difference being found between the two.