Report di benchmark
Metodologia e risultati per scenario: Codna vs. i principali agenti di coding.
Risorse
Guide, benchmark e asset di lancio.
Scopri come la comprensione deterministica del codebase cambia la riparazione autonoma del codice.
Risorse
Apri
Riparazione autonoma del codice
Perché gli agenti hanno bisogno di una mappa deterministica prima di correggere.
Revisione del codice AI
Come il blast radius e il rischio di regressione rendono le revisioni delle PR più veloci.
Da Sentry alla PR
Trasforma i fallimenti in produzione in pull request di correzione mirate.
Frequently asked
Codna was tested head-to-head against Cursor. It used 5× fewer tokens and ran 1.7× faster, with every fix test-verified.
Token consumption, wall-clock speed, and verified fix rate across 8 real bug-fix scenarios run against OpenAI Codex CLI and Google Gemini CLI. Every fix counted only when your own tests passed.
A deterministic engine maps the repository in roughly 60ms without any LLM calls. It then hands the AI agent an evidence bundle of around 600 tokens — measured 162x smaller than reading the full repo — so the agent fixes the right code immediately.
Yes — Codna supports 250+ languages, and the engine mapped 130 repositories in 9.2 seconds, consuming zero tokens for the mapping step.
Codna locates the affected code using its dependency and blast-radius graph, generates a fix from the evidence bundle, then runs your tests to verify the result. On GitHub, it opens a pull request with the verified fix.
You can self-host the engine, bring your own API key, and configure fail-closed egress. Codna never trains on your code.