Treffer: An Iterative Group-Based MOPSO with Isomap-Guided Leaders and DQN-Adaptive Parameters for Automated Path Coverage Test Case Generation.

Automated Test Case Generation for Path Coverage (ATCG-PC) is a critical yet challenging task in software testing, especially for large-scale programs where metaheuristic algorithms often suffer from premature convergence and inefficient exploration. This paper proposes a novel algorithm, Isomap-DQN-MOPSO (IDMOPSO), which significantly enhances the Multi-Objective Particle Swarm Optimization (MOPSO) framework. Our approach introduces an iterative, prefix-based path grouping strategy to manage complexity. Crucially, it integrates two machine learning-based enhancements: an Isomap manifold learning strategy for more effective leader selection to guide the swarm and escape local optima, and a hybrid Deep Q-Network (DQN) for dynamically adapting learning factors to balance exploration and exploitation. Comprehensive experiments on a diverse set of 18 programs demonstrate that IDMOPSO achieves superior performance, particularly on large-scale programs where it attains significantly higher path coverage rates than state-of-the-art methods. Ablation studies confirm the synergistic effect of combining Isomap and DQN, validating our approach as a robust and scalable solution for complex ATCG-PC problems. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)