experiment

Code optimization results are tracked by logging each iteration as KEPT, DISCARDED, or FAILED with hypotheses and notes. This builds institutional memory, ensuring failed or regressive code changes are not re-attempted in future optimization cycles.

A single-change code iteration strategy implements and tests one specific modification at a time against baseline metrics. This experimental discipline ensures deterministic, verifiable performance tuning cycles by isolating the impact of each individual code change.

Experiment outcomes are logged by recording each code modification as KEPT, DISCARDED, or FAILED alongside its hypothesis and notes. This constraint management approach tracks discarded strategies to avoid redundant optimization efforts.

Predefined tests and baseline metrics are required to execute deterministic and verifiable code optimization experiments. Adherence to configuration and learned constraints ensures each single-change iteration produces a valid, testable outcome.

Constraint management works by learning from past experiments to avoid repeating ineffective code modification strategies. It enforces experimental discipline by tracking discarded approaches, ensuring future iterations respect previously established constraints and baseline metrics.

Testing multiple changes simultaneously is not supported; the approach executes a single, testable code optimization iteration at a time. This ensures deterministic results by isolating each modification against predefined tests and baseline metrics.