CIRCLE: continual repair across programming languages
Wei Yuan, Quanjun Zhang, Tieke He, Chunrong Fang, Quoc Viet Hung Nguyen, Xiaodong Hao, Hongzhi Yin
Abstract
Automatic Program Repair (APR) aims at fixing buggy source code with less manual debugging efforts, which plays a vital role in improving software reliability and development productivity. Recent APR works have achieved remarkable progress via applying deep learning (DL), particularly neural machine translation (NMT) techniques. However, we observe that existing DL-based APR models suffer from at least two severe drawbacks: (1) Most of them can only generate patches for a single programming language, as a result, to repair multiple languages, we have to build and train many repairing models. (2) Most of them are developed offline. Therefore, they won’t function when there are new-coming requirements.