Code Completion
Code completion automates the suggestion of code snippets during software development, aiming to boost developer productivity and code quality. Current research emphasizes improving the accuracy and efficiency of these tools, particularly focusing on leveraging large language models (LLMs) and incorporating repository-level context through techniques like retrieval augmentation and graph-based approaches. This field is crucial for enhancing software development workflows, with ongoing efforts addressing challenges such as security vulnerabilities in LLM-based tools and the need for more realistic and comprehensive evaluation benchmarks.
Papers
A Static Evaluation of Code Completion by Large Language Models
Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
Tianyang Liu, Canwen Xu, Julian McAuley