A Lightweight Fault Localization Approach based on XGBoost

Abstract

Software fault localization is one of the key activities in software debugging. The program spectrum-based approach is widely used in fault localization. However, lots of program information, for example, the sequence of the execution statement and statement semantics, is missing when such an approach is utilized, which affects the performance. XGBoost is an effective learning algorithm, which can use the characteristics of the training data to build a classification tree during training. In addition, XGBoost can iteratively adjust the information value of the feature, so that the training process retains the importance information of the feature. This paper proposes applying XGBoost into fault localization utilizing information of program execution behaviors. A novel method called XGB-FL is developed, where the program spectrum information is converted into a coverage matrix to train the XGBoost model. We can get the characteristics of the data through the trained model and the importance of the program statement in the classification process. This is also the basis for judging whether the statement is likely to contain a fault. Nine representative data sets have been chosen to evaluate the performance of XGB-FL. The experimental results show that XGB-FL can generally deliver a higher performance in fault localization than those baseline techniques, in terms of precision and efficiency.