A Fault Localization Approach Based on BiRNN and Multi-Dimensional Features
Abstract
Software fault localization is notoriously tedious and time-consuming. Developed rapidly, machine learning techniques have been adopted for fault localization by researchers. Most existing approaches use the test coverage information as feature input to the learning model, ignoring the limited ability of the single-dimensional features. The effectiveness of fault localization is not greatly improved. To overcome the limitation, we propose a fault localization approach based on Bidirectional Recurrent Neural Networks (BiRNNs) and multi-dimensional features. Our approach collects suspiciousness-based, text similarity-based and fault-proneness-based features from the traditional fault localization areas and software metrics. To evaluate our approach, the experiments have been studied on the real-fault benchmark Defects4J and seeded fault program NanoXML. The experimental results show that our approach effectively improves fault localization accuracy.
