Show HN: FKS2G – LLM-backed metrics for deciding how closely to review code (github.com)

FKS2G is a novel command-line interface (CLI) tool aimed at enhancing the code review process by leveraging large language models (LLMs). As code review becomes a significant bottleneck in software development, FKS2G helps developers gauge how closely they should review code changes. It utilizes multiple metrics, including cosine similarity between file-name embeddings and relevant project texts, LLM evaluations of file change likelihood, recent bug-fix commit classifications, change frequency from git history, and ultimately presents a risk assessment based on gathered evidence. This tool is particularly significant for the AI/ML community as it addresses common pain points in code reviews, such as the risk of overlooking potential issues and finger-pointing when defects arise after deployment. By integrating LLMs like ChatGPT for analysis, FKS2G automates the risk evaluation process, which can lead to more efficient code review cycles and improved software quality. Accessible with a simple command, it supports other models in addition to OpenAI, suggesting its versatility. As FKS2G matures, it presents a promising shift towards more data-driven approaches in software development, potentially transforming how code review is approached across teams.