Hybrid artificial intelligence for multi fault diagnosis in software systems using chaotic genetic algorithm and artificial bee colony optimization

Abstract Fault diagnosis in complex software systems remains a critical challenge in software engineering, particularly under multi-fault and reliability-critical settings. To address the limitations of traditional spectrum-based fault localization techniques, such as poor search efficiency and susceptibility to local optima, this paper proposes a hybrid artificial intelligence framework that integrates Artificial Bee Colony (ABC) optimization with a Chaotic Genetic Algorithm (CGA). Experimental results on the Defects4J benchmark demonstrate a 12.1% improvement in Top-5 accuracy and a 38% reduction in EXAM score compared to particle swarm optimization-based fault localization methods. The proposed framework exploits the global exploration capability of ABC and the intensified local refinement enabled by chaotic operators in CGA to improve fault ranking while avoiding premature convergence. A dynamically weighted fitness function combines information from spectrum-based fault localization metrics, ABC, and CGA to compute final suspicion scores. Statistical significance analysis using the Wilcoxon signed-rank test ( p = 0.007) confirms the effectiveness of the proposed approach, highlighting its potential to support automated debugging in large-scale and distributed software systems.