Improving bug detection and fixing via code representation learning
Yi Li · 2020
The software quality and reliability have been proved to be important during the program development. There are many existing studies trying to help improve it on bug detection and automated program repair processes. However, each of them has its own limitation and the overall performance still have some improvement space. In this paper, we proposed a deep learning framework to improve the software quality and reliability on these two detect-fix processes. We used advanced code modeling and AI models to have some improvements on the state-of-the-art approaches. The evaluation results show that our approach can have a relative improvement up to 206% in terms of F-1 score when comparing with baselines on bug detection and can have a relative improvement up to 19.8 times on the correct bug-fixing amount when comparing with baselines on automated program repair. These results can prove that our framework can have an outstanding performance on improving software quality and reliability in bug detection and automated program repair processes.