Deep Just-In-Time Defect Prediction Based on Double-Source Input Self-Attention Mechanism (S)

Ensuring high-quality software products is an important issue for software productions, it emerged Just-In-Time Quality Assurance to automatically identify potentially defective code as early as possible in recent years.The presented models mainly utilize Convolutional Neural Networks for automatic defect feature extraction.However, these models ignore the utilization of contextual information and are not suitable for large-scale projects.To address these issues, we propose a model named Multi-head Convolution Structure of Attention-JIT (short for MCSA-JIT), which comprises four layers.Specifically, Commit Message-Code Change(short for CM-CC) Text Feature Extraction Layer leverages a variant of the self-attention mechanism to extract features from both commit messages and code changes, CM-CC Structure Feature Extraction Layer captures the content information of the submissions and different CNN structures are constructed to extract structural information, Feature Combination Layer combines the text feature and structure feature to predict defects and output layer outputs the final value.Experimental results conducted on two software projects, QT and OPENSTACK, demonstrate that the best variant of MCSA-JIT achieves a relative improvement of 7.46% in terms of AUC on the OPENSTACK project and a relative improvement of 8.85% on the QT project when compared to state-of-the-art methods with the best performance.