A Survey on Deep Learning-Based Source Code Defect Analysis
Zhibin Guan, Xiaomeng Wang, Xin Wei, Jiajie Wang, Zhang Li · 2020 5th International Conference on Computer and Communication Systems (ICCCS) · 2020
With the rapid development of information technology, various software applications are flooding our daily lives. The development of these application software inevitably generates a lot of source code. How to detect and analyze various defects in the source code, such as API/Function call errors, array misuse, and expression syntax error, etc., which is known as source code defect analysis (SCDA), has attracted the attention of many researchers in the academic field. Since artificial intelligence (AI) technology has achieved excellent results in the field of image processing and natural language processing, researchers have tried to use deep learning algorithms in AI to automatically extract and analyze features of source code. Therefore, we review the recent deep learning-based source code defect analysis methods, including abstract syntax tree-based methods, program dependency graph-based methods, and other deep learning-based methods. Compared to traditional methods, the deep learning-based code defect analysis methods can realize the automatic extraction of source code defect features. This means that there is no longer a need for human experts to pre-define code features, which avoids errors caused by humans to a certain extent. The application research of AI in the source code defect analysis is an interesting and challenging development direction, and we believe it has broad development prospects.