BCL-FL: A Data Augmentation Approach with Between-Class Learning for Fault Localization

Automated fault localization (FL) techniques collect runtime information as input data and then analyze input data to identify the relationship between program statements and failures. They usually take advantages of the statistics of the input data to develop a suspiciousness evaluation methodology (e.g., spectrum-based formulas and deep neural network models) by exploring the underlying correlation rooted in the input data. Thus, the quality of input data is critical for FL. In the actual process of development, developers seek to generate adequate test cases for testing the function or the robustness of a subject program. However, regarding a fault, most test cases are passed test cases and a very few ones are failed test cases since a very small portion of inputs in input domain will lead to a program failure. It means that FL usually faces a problem of imbalanced data, and this problem has been proven to pose an adverse effect on FL effectiveness. To address this problem, we propose BCL-FL: a data augmentation approach based on between-class learning, which produces new synthesized failed test samples by mixing two classes of real test cases (i.e., a passed test case and a failed one) with a random ratio. Specifically, BCL-FL uses the characteristics of real failed test cases to design a data synthesis formula suitable for failed test samples, which can make the synthesized failed test samples closer to real test cases. Since the synthesized data is different from real data, we ingeniously assign a continuous value between 0 and 1 to label the synthesized sample according to the mixing ratio of original labels. We take the synthesized failed test samples and the original test cases as the balanced input data for FL techniques to address the imbalanced data problem. To evaluate the effectiveness of BCL-FL, we conduct large-scale experiments on 287 faulty versions of eight large-sized programs (from ManyBugs and Defects4J) using six state-of-the-art FL approaches. The experimental results show that BCL-FL significantly improves the effectiveness of existing FL techniques, e.g., BCL-FL improves the CNN-FL approach in Top-1, Top-5, and Top-10 by 150%, 136.36%, and 193.1%, respectively.