openclaw-test-performance

You can reduce OpenClaw test runtime by automating benchmarking to locate hotspots, diagnosing CPU and RSS memory usage, and applying targeted fixes. This approach identifies import hotspots and slow coverage paths while preserving coverage integrity.

The best way to profile memory and CPU usage in a pnpm test suite is to establish a baseline benchmark, use perf-guided commands to surface hotspots, and analyze structured results to guide iterative tuning and verify gains.

Yes, this benchmarking approach applies to local OpenClaw projects using pnpm test. It requires baseline commands and optional import-durations breakdown to surface regression points across the test suite.

Import hotspots and slow coverage paths occur when specific modules consume excessive CPU or wall time during the test run. Diagnosing these hotspots through baseline benchmarking helps identify the exact bottlenecks causing the slowdown.

Yes, you need baseline commands to establish a reference point before optimizing. After running targeted optimizations, re-measuring against this baseline validates performance gains and ensures coverage has not regressed.

Diagnosing RSS memory usage helps optimize test performance by identifying memory-intensive tests that cause bottlenecks. Targeting these specific areas for fixes reduces the overall memory footprint without regressing test coverage.