Show HN: FKS2G – LLM-backed metrics for deciding how closely to review code
FKS2G is a tool designed to assist developers in determining the level of scrutiny required during code reviews. It utilizes various metrics, including LLM assessments and historical data, to evaluate the risk associated with code changes. The software aims to streamline the review process and reduce the likelihood of shipping problematic code.
- ▪FKS2G helps developers decide how closely to review code changes.
- ▪It collects data such as cosine similarity and LLM judgments to inform review depth.
- ▪The tool is currently in a 0.X software stage, indicating it is still in development.
Opening excerpt (first ~120 words) tap to expand
fks2g Since code review is the bottleneck now, fks2g helps developers decide how closely to review code. Its for the devs who have already tried this method of reviewing code: And for devs who have realized that this code review strategy leads to a finger pointing situation when bugs or bad architecture gets shipped: To inform how closely to review a code change, the CLI collects: cosine similarity between file-name embeddings and configurable project text sources an LLM judgment about whether the closest files are likely to change soon based on source documents recent bug-fix commits classified by an LLM file change frequency from git history an LLM final risk assessment based on the collected evidence Usage OPENAI_API_KEY=<KEY> npx fks2g analyze -- --repo ../react --github-repo…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.