Software Fault Localization Based on Graph Interpretable Networks
Software fault localization aims to improve the accuracy of localization by mining program and execution data of test ca-ses.To address the issue that spectrum-based fault localization(SBFL)technology relies too much on binary coverage information,a software fault localization method based on graph-interpretable networks is proposed.This method transforms test execution into a graph structure,utilizes the graph attention network modeling to deeply mine the implicit information and interrelationships among code segments.The reinforcement learning principles are used to explain the decision-making process after the graph attention net-work learning,thereby determine the key nodes and reduce the fault localization range.The experiments are conducted on five pro-jects from the Defects4j dataset and compared with the SBFL and uninterpreted deep learning methods.The results show that based on graph-interpretable networks,the localization method improves the Top-1,Top-3,and Top-5 indicators by 7.26％,7.56％,and 9.96％,respectively,and the EXAM index also increases by 8.98％,significantly outperforming other methods.
software testingfault localizationprogram spectrumgraph attention networkinterpretable model
NETL
NSTL
万方数据
