The adoption–trust gap
In Stack Overflow’s 2025 developer survey, 84% of respondents used or planned to use AI tools, while 46% distrusted their accuracy. The common complaint was output that is almost right but still expensive to debug. That tension—not a claim that all generated code is bad—is the product premise.
Source: Stack Overflow Developer Survey 2025
Supply-chain foundations
- SLSA v1.2 defines provenance and build integrity concepts used by the build-once, promote-by-digest model.
- OpenSSF Scorecard provides measurable signals for risky open-source repository practices.
- GitHub Actions security hardening documents permission, pinning, and untrusted-input boundaries.
Testing as evidence
Coverage answers what executed; mutation testing asks whether tests discriminate between correct and deliberately damaged behavior. MergeGrounds requires both where policy materializes them and treats incomplete reports as not evaluated.
Source: Mutation Testing Elements
Research policy
We separate verified facts, local observations, and product hypotheses. Pages that compare tools include a checked date and link to primary documentation. Benchmarks must publish fixtures, versions, raw results, and limitations before they become marketing claims.
AI-enabled products need another evidence plane
When a model, retrieval pipeline, or agent is shipped behavior, source checks alone cannot validate stochastic output. Our public assurance guide translates research on unfaithful reasoning, delivery speed, RAG, long context, benchmark contamination, and fine-tuning regression into falsifiable engineering controls.