A LambdaMart-Based High-Accuracy Approach for Software Automatic Fault Localization

Software debugging or fault localization is a very significant task in software development and maintenance, which directly determines the quality of software. Traditional methods of fault localization rely on manual investigation, which takes too much time in large-scale software development. To mitigate this problem, many automatic fault localization techniques have been proposed which can effectively lighten the burden of programmers. However, the quality of these techniques is not enough to meet the practical requirements. In order to improve the accuracy of fault localization, we propose LBFL, a LambdaMart-based high-accuracy approach for software automatic fault localization, which can integrate software’s diversified features and achieve very high accuracy. To realize that, LBFL first extracts the static and dynamic features and normalizes them. Then these features are gathered on LambdaMart algorithm for training. Finally, LBFL sorts the code statements according to the model and generates a list which can help developers to locate faults. Exhaustive experiments indicate that LBFL can locate 76 faults in Top-1, which has at least 217% improvements over nine single techniques and has 55% improvements over ABFL approach on the Defects4J dataset.