Summary of A Comprehensive Survey Of Ai-driven Advancements and Techniques in Automated Program Repair and Code Generation, by Avinash Anand et al.
A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation
by Avinash Anand, Akshit Gupta, Nishchay Yadav, Shaurya Bajaj
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive survey of 27 recent papers on Large Language Models (LLMs) in Automated Program Repair (APR) and code generation has been conducted, highlighting the transformative impact of LLMs on bug fixing and code generation. The survey reviews new methods for detecting and repairing semantic errors, security vulnerabilities, and runtime failure bugs using APR with LLMs, emphasizing their potential to reduce manual debugging efforts by up to 85%. Additionally, the paper explores general-purpose LLMs fine-tuned for programming, task-specific models, and methods to improve code generation, including identifier-aware training, instruction-level fine-tuning, and incorporating semantic code structures. The survey also discusses the challenges of achieving functional correctness and security in LLM-based software development and identifies future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study looks at how powerful computer programs called Large Language Models can help fix bugs in computer code and even write new code for us! Researchers reviewed 27 recent papers on this topic and found that these models are very good at helping computers find and fix errors. They also learned how to make the models better by training them to understand programming languages and writing code in a specific way. This could be really helpful for people who don’t have much experience with coding, like students or non-technical adults. The study also talked about some of the challenges that come with using these powerful models, but overall it shows how they can make software development easier and more efficient. |
Keywords
» Artificial intelligence » Fine tuning
