Can RVEA with DynaMOSA features perform well at generating test cases?

Bachelor thesis (2023)

Authors

S. Datskiv 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 Sergey Datskiv
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

On the intuitive level, software testing is important because it assures the quality of the software used by humans. However, ensuring this quality is not an easy task because as the complexity of the software increases, so do the efforts to test it. Search-based software testing is an active research field that develops and explores tools for automatic test case generation. Their work involves using meta-heuristic search optimisation approaches such as evolutionary algorithms
from the evolutionary computation community and applying them to test case generation. The crossover between the evolutionary computation domain and the test case generation one produced DynaMOSA, a state-of-the-art evolutionary algorithm for generating test cases. In an attempt to
produce another well-performing algorithm, this paper performs another crossover between the two communities and creates DynaMOSARVEA - a product of a Reference Vector Guided Evolutionary Algorithm (RVEA) and DynaMOSA.
The conducted empirical study showed that although DynaMOSARVEA does not outperform DynaMOSA, it did outperform RVEA, thus demonstrating the value brought by domain-specific knowledge.