CIRCLE: Continual Repair Across Programming Languages | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A continuously updated collection of research papers on LLMs. Maintained by Sean Moran.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

CIRCLE: Continual Repair Across Programming Languages

Wei Yuan, Quanjun Zhang, Tieke He, Chunrong Fang, Nguyen Quoc Viet Hung, Xiaodong Hao, Hongzhi Yin . Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis 2022 – 48 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Compositional Generalization Content Enrichment Image Text Integration Interactive Environments Interdisciplinary Approaches Llm For Code Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Prompting Question Answering Tools Variational Autoencoders Visual Question Answering

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 in an offline manner. Therefore, they won’t function when there are new-coming requirements. To address the above problems, a T5-based APR framework equipped with continual learning ability across multiple programming languages is proposed, namely ContInual Repair aCross Programming LanguagEs (CIRCLE). Specifically, (1) CIRCLE utilizes a prompting function to narrow the gap between natural language processing (NLP) pre-trained tasks and APR. (2) CIRCLE adopts a difficulty-based rehearsal strategy to achieve lifelong learning for APR without access to the full historical data. (3) An elastic regularization method is employed to strengthen CIRCLE’s continual learning ability further, preventing it from catastrophic forgetting. (4) CIRCLE applies a simple but effective re-repairing method to revise generated errors caused by crossing multiple programming languages. We train CIRCLE for four languages (i.e., C, JAVA, JavaScript, and Python) and evaluate it on five commonly used benchmarks. The experimental results demonstrate that CIRCLE not only effectively and efficiently repairs multiple programming languages in continual learning settings, but also achieves state-of-the-art performance with a single repair model.

Similar Work