Multiple fault localization based on ant colony algorithm via genetic operation

Abstract

Debugging programs involves a considerable amount of time and resources spent on identifying the source of errors. Spectrum-based fault localization techniques are becoming increasingly popular due to their quick and effective performance when dealing with programs that have a single fault. However, these methods do not take into account the reality that most programs tend to have multiple faults. To address the above challenges, we propose an automatic approach using ant colony algorithm via genetic operation for multiple fault localization (ACOMFL). Our approach first constructs the search graph by the coverage information. Secondly, it designs the fitness function to select the best paths and dynamically adjusts the pheromone updating. Finally, it computes the suspiciousness ranking of each statement by the ant path information in the optimal path set. Four open-source benchmarks are utilized to validate the performance of the ACOMFL approach. The experimental results show ACOMFL achieves better results for multi-fault programs in comparison with six spectrum-based fault localization approaches and a genetic algorithm-based multiple fault localization approach. The ACOMFL is effective at locating multi-fault, serving as a helpful method for locating faults.