OpenClaw prunes 400,000 lines of redundant tests

AI-assisted testing has easily gamified code coverage metrics at the cost of massive test bloat and maintenance debt.

* Frontier models default to producing brittle, implementation-coupled tests for every code edit, rapidly inflating codebase size without safeguarding true behavioral invariants.

* Vague cleanup prompts fail with autonomous agents; models require explicit, bounded targets (e.g., pruning a specific percentage of tests within tight coverage bounds) to drive meaningful refactoring.

* As LLMs demonstrate higher autonomous persistence across iterations, test-authoring gates and retention policies will become vital guardrails against synthetic mock inflation.

* Automated testing governance will shift from generating coverage to evaluating test necessity and decoupling validation from internal implementation seams.