Testing AI Coding Agents Beyond Code Generation: A Real-World Benchmark
A developer published a benchmark on DEV Community that tests AI coding agents beyond simple code generation, using a real-world full-stack task-manager project. The benchmark documents two Codex runs: Phase 1 was a greenfield build from an empty directory, and Phase 2 returned to the existing application for an authentication and database-migration refactor, which also included a long, user-initiated interruption. The initial application was recorded as complete in approximately 34 minutes, and the later refactor took approximately 42 minutes of active execution, excluding the pause. The Phase 2 run reported 16 backend tests, 1 legacy-migration test, and 25 frontend tests passing, along with lint, type, build, and dependency checks. The author argues that most public demonstrations measure only the first and easiest part of software engineering—generation—while real projects are stateful, containing existing behavior, data that must survive, security boundaries, partially completed work, and verification steps. This benchmark serves as a small, practical record of that harder problem, showing that AI agents can handle stateful refactoring and recovery from interruptions, though the results are specific to this project and tooling.
Demonstrates that AI coding agents can handle stateful refactoring and interruptions, not just greenfield generation.