Why existing approaches fall short

Test selection isn't new, and good tools exist. Going by each project's public docs as of October 2026 (corrections welcome):

• Coverage-based selection (Datadog Test Impact Analysis, pytest-testmon) records which tests execute which code. That sees dynamic dependencies a static graph misses, but needs instrumented runs; Datadog's is SaaS, and testmon is a local plugin with no server.
• Predictive selection (Develocity Predictive Test Selection, CloudBees Smart Tests) learns from past builds. Develocity is self-hosted but built for Gradle and Maven; neither is open source.
• Path filters are cheap and risky. On kubo, mapping changed files to tests by name caught 1 of 9 failures with the fallbacks switched off: the failures were in packages that depend on the change.

At the time of writing, I didn't find agent or MCP support documented for these tools. That gap, plus self-hosting with no instrumentation, is where siftwise sits.