AI Gap Analysis — Know Exactly What's Implemented, What's Missing, and What's Gone Wrong.
WalnutAI's 3-phase AI requirements gap analysis finds missing, incomplete, and outdated code against user stories, validates story accuracy, and scores code quality — a shift-left testing tool that catches gaps before every release.
Trusted by teams shipping with confidence before every release
More expensive to fix a defect in production than at the requirements stage
Fewer production defects reported by teams using WalnutAI gap analysis and test generation
Missing code detection, story validation, and code quality scoring — in a single automated run
The bugs you catch before QA cost 100X less than the ones you don't
IBM's System Sciences Institute study found that fixing a defect in production costs 100 times more than fixing it during the requirements phase. WalnutAI's gap analysis is the mechanism that closes that window — running a semantic comparison of every story against your actual code before the sprint ends, not after the release ships. Teams using WalnutAI's gap analysis pipeline have reported up to 50% fewer production defects escaping to release.
Find the code gaps before QA finds them
Phase 1 semantically compares every user story and acceptance criterion against your actual source code — returning a gap category (Missing, Incomplete, or Outdated), a confidence score from 0–100%, and the exact code snippets that are relevant. No more guessing what got built and what didn’t.
Validate whether your stories are still accurate
Phase 2 checks the stories themselves — not just the code. It detects duplicates above 85% semantic similarity, compares story descriptions against what the code actually implements, and suggests corrections to titles and acceptance criteria. You review and accept or reject each suggestion individually.
Score code quality per story and get a report you can act on
Phase 3 scores every user story’s code from 0–100 across complexity, pattern adherence, and anti-patterns — then goes further. Architectural gaps, security vulnerabilities (OWASP-class), missing unit tests, and undocumented code are all surfaced per story. A PDF report with annotated snippets and prioritized fixes lands ready for your next sprint or leadership review.
Works across any scale of codebase and repository setup
Repositories up to 10GB and 100,000+ files are supported across GitHub, GitLab, Bitbucket, and Azure DevOps (cloud and self-hosted). Multi-repository projects are analyzed together with results aggregated into a single view.
Ready to ship with confidence?
See how WalnutAI connects requirements, code, testing, and deployment into one intelligent workflow.