Augmenting Pareto Corner Search Evolutionary Algorithm for Automatic Test Case Generation

Bachelor thesis (2023)

Authors

E.A. Güneri Electrical Engineering, Mathematics and Computer Science

Contributors

A. Panichella Software Engineering - EEMCS (supervisor 1)
Mitchell Olsthoorn Software Engineering - EEMCS (supervisor 1)
D.M. Stallenberg Software Engineering - EEMCS (supervisor 1)
S.E. Verwer Cyber Security - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Alp Güneri
More Info
expand_more

Published Date

28-06-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Software testing is a laborious job, and accounts for a large portion of software development expenses. Search-based automatic test case generation is an area of research that attempts to remedy this by discovering algorithms suited for generating test cases automatically. In this field, DynaMOSA is a state-of-the-art evolutionary algorithm, which reduces the problem of test case generation to a multi-objective optimization problem, and uses domain knowledge to generate solutions efficiently. In this paper, we adapt Pareto Corner Search Evolutionary Algorithm (PCSEA) for test case generation. Furthermore, we integrate DynaMOSA heuristics into PCSEA, and create a novel algorithm, DynaMOSAPCSEA. We evaluate the test case generation efficacy of PCSEA, DynaMOSA, and DynaMOSAPCSEA by using the JavaScript benchmark provided by SynTest Framework. The results indicate that PCSEA is a feasible algorithm for test case generation, however DynaMOSA heuristics improve its performance only minimally.