Evaluating AI code-generation assistants in production
Evaluate code-generation assistants with accepted outcomes, tests, security, maintainability, developer corrections, latency, and cost while respecting privacy.
Production evaluation for AI code-generation assistants should measure whether suggestions become correct, maintainable, secure code with less developer effort. Acceptance rate alone is incomplete: developers may accept and heavily edit a suggestion, or reject a correct completion because it arrived too late.
Define observable outcomes
Track suggestion shown, accepted, modified, reverted, and retained after a useful window. Link accepted changes to tests, static analysis, review findings, and incidents where lawful and privacy-preserving.
Useful metrics include accepted-and-retained rate, edit distance, tests passed, security findings, time to completion, latency, and cost. Report by language, repository type, task, and suggestion size.
Build offline repository tasks
Use pinned commits and representative bugs, functions, tests, refactors, and documentation tasks. Include project conventions and hidden acceptance tests. Prevent benchmark leakage and unrestricted network access.
Trace context retrieval, prompt assembly, generation, tools, tests, and revisions. Store hashes or approved snippets rather than sensitive source when full content is not permitted.
Evaluate safety and maintainability
Run linters, type checks, dependency policy, secret scanning, and security tools. Test whether the assistant introduces unsafe APIs, weakens validation, or copies untrusted instructions from comments and issues.
Human review remains important for architecture and subtle maintainability. Calibrate any LLM reviewer against real code-review decisions before scaling it.
Measure developer effort
Compare time and correction effort for matched tasks with and without assistance. Do not rank individual developers. Analyze workflows and product versions, and account for task difficulty.
Learn from production responsibly
Collect only telemetry users and organizations have authorized. Redact secrets, respect repository boundaries, and use documented retention. Sample reversions, large edits, failed tests, and low-latency rejections for qualitative review.
Currai can connect generation metadata, tool spans, latency, tokens, and cost to an evaluation outcome. For autonomous jobs, use the more extensive asynchronous coding-agent evaluation; for interactive assistants, focus on retained quality and developer effort.