Authors: Zeeshan Rasheed, Malik Abdul Sami, Muhammad Waseem, Kai-Kristian Kemell, Xiaofeng Wang, Anh Nguyen, Kari Systä, Pekka Abrahamsson

Published on: April 29, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2404.18496

Summary

• What is new: A novel LLM-based AI agent designed to review code, predict future risks, and support developer education by teaching best practices and efficient coding techniques.
• Why this is important: The need to improve software quality and efficiency, beyond what traditional static code analysis tools can achieve.
• What the research proposes: A sophisticated LLM-based model trained on large code repositories, capable of identifying code issues, suggesting improvements, and predicting future risks.
• Results: The model has shown to significantly reduce post-release bugs and positively impact the code review process, with developers responding favorably to LLM feedback.

Technical Details

Technological frameworks used: Large Language Models (LLMs)

Models used: LLM-based AI agent

Data used: Code reviews, bug reports, documentation of best practices

Potential Impact

Software development sector, specifically companies focused on code analysis tools, software quality assessment, and developer education platforms.

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