On Test Sequence Generation using Multi-Objective Particle Swarm Optimization

• URL: http://arxiv.org/abs/2404.06568v1
• Date: Tue, 9 Apr 2024 18:35:21 GMT
• Title: On Test Sequence Generation using Multi-Objective Particle Swarm Optimization
• Authors: Zain Iqbal, Kashif Zafar, Aden Iqbal, Ayesha Khan,
• Abstract summary: Software testing is an important and essential part of the software development life cycle. In the software industry, testing costs can account for about 35% to 40% of the total cost of a software project.
• Score: 0.2999888908665658
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Software testing is an important and essential part of the software development life cycle and accounts for almost one-third of system development costs. In the software industry, testing costs can account for about 35% to 40% of the total cost of a software project. Therefore, providing efficient ways to test software is critical to reduce cost, time, and effort. Black-box testing and White-box testing are two essential components of software testing. Black-box testing focuses on the software's functionality, while White-box testing examines its internal structure. These tests contribute significantly to ensuring program coverage, which remains one of the main goals of the software testing paradigm. One of the main problems in this area is the identification of appropriate paths for program coverage, which are referred to as test sequences. Creating an automated and effective test sequence is a challenging task in the software testing process. In the proposed methodology, the challenge of "test sequence generation" is considered a multi-objective optimization problem that includes the Oracle cost and the path, both of which are optimized in a symmetrical manner to achieve optimal software testing. Multi-Objective Particle Swarm Optimization (MOPSO) is used to represent the test sequences with the highest priority and the lowest Oracle cost as optimal. The performance of the implemented approach is compared with the Multi-Objective Firefly Algorithm (MOFA) for generating test sequences. The MOPSO-based solution outperforms the MOFA-based approach and simultaneously provides the optimal solution for both objectives.

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