Performance of the Pareto Envelope-Based Search Algorithm - II in Automated Test Case Generation

Bachelor thesis (2023)

Authors

A. Abhishek 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 Apoorva Abhishek
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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 yet time consuming task in the software development life cycle. Artificial Intelligence (AI) algorithms have been used to automate this task and have proven to be proficient at it. This research focuses on the automated testing of JavaScript programs, and builds upon the existing SynTest framework that is the current state of the art, with the Dynamic Many Objective Sorting Algorithm (DynaMOSA) being the best performing AI algorithm for test case generation. DynaMOSA uses the Non-Dominated Sorting Algorithm - II (NSGA-II) as its base algorithm, and adds modifications to it. This paper investigates whether the use of the Pareto Envelope Based Search Algorithm - II (PESA-II) as the base algorithm results in improved performance. The contributions of this research includes a modified PESA-II integrated into the SynTest framework, using inspiration from DynaMOSA. Moreover, we answer the question "How does the modified PESA- II perform compared to DynaMOSA in generating test cases for JavaScript programs?" The performance of the algorithms is measured based on the (branch and method) coverage of the test cases generated for a suite of JavaScript classes. The results show that the modified version of PESA-II outperforms the base version. However, neither manage to outperform DynaMOSA.