autoresearch

Automated iterative code improvement runs experiment cycles that establish a baseline, implement a hypothesis, measure metric changes, and log beneficial modifications in git while reverting regressions. You configure a metric command and a target condition to start the autonomous optimization loop.

Autonomous code experimentation with git branching commits each attempted improvement to a new branch with a descriptive message of the hypothesis. This mechanism allows you to review the autonomous changes and revert any modifications that cause metric regressions.

Yes, you can apply optional scope controls to limit edits to specific file sets during the autonomous improvement loop. Scoping ensures the automated optimization and experimentation only modifies targeted code areas within your project.

Autonomous code optimization applies to software projects across languages by using configurable metrics. You supply a metric command and target condition, allowing the iterative improvement loop to run language-agnostic experiment cycles for tests or quality gates.

You can configure an optional max iterations parameter to limit the number of autonomous experiment cycles. Setting a maximum iteration count prevents the improvement loop from running indefinitely while it attempts to satisfy your target metric condition.

Automated iterative code improvement relies on measurable metric commands and target conditions to function properly. If your project lacks quantifiable benchmarks, test suites, or quality gates, the autonomous loop cannot evaluate hypotheses or decide whether to commit or revert changes.