Why 100% Line Coverage is Lying to You in AI-Generated Code (And How We Catch It)

A developer has open-sourced DeployProof, a Python pre-push verification tool that uses diff-scoped AST mutation testing to catch hollow test suites, hallucinated dependencies, and security traps in AI-generated code. The tool reduces verification time from minutes to seconds by targeting only changed lines, and it flags issues like unasserted logic and hardcoded secrets before deployment.

If you have spent the last few months building projects with AI coding assistants Antigravity, Claude Code, Cursor, Copilot , you have likely experienced this specific frustration: You prompt an agent to build a feature or fix a bug. The agent writes tests. You run pytest , and all green checkmarks appear with 100% line coverage . You feel confident and push to production — only to discover after deployment that the tests were completely hollow and missed critical edge-case logic. Line coverage measures whether a line of code was executed , not whether its logic was actually asserted . To solve this, I built and open-sourced DeployProof — a deterministic pre-push verification tool for Python that catches hollow test suites, hallucinated dependencies, and security traps in seconds before code leaves your local machine. To illustrate the problem clearly, consider this simple discount calculator with a 50% threshold cap: php calculator.py def calculate discount price: float, rate: float - float: if rate 0.5: return price 0.5 return price 1.0 - rate When asked to write unit tests, an LLM might generate this: python test calculator.py from calculator import calculate discount def test calculate discount standard : assert calculate discount 100.0, 0.2 == 80.0 This single test hits every branch of the standard discount and yields 100% line coverage . However, if you mutate the logic: rate 0.5 to rate 1.5 return price 0.5 to return price 1.5 to / The test suite still passes 100% green. The test never asserted the threshold cap or boundary conditions. Traditional mutation testing tools like mutmut or cosmic-ray are powerful, but they typically run against the entire codebase. On a project with hundreds of tests, running a full mutation suite can take 5 to 20 minutes — far too slow to run on every git commit or pre-push hook. DeployProof solves this with Diff-Scoped AST Mutation: Instead of mutating the entire repository, DeployProof inspects your active git diff or uncommitted session files and targets AST mutations strictly to the lines you just wrote or modified. This drops verification time from minutes down to 2 to 4 seconds . bash $ deployproof check DeployProof - LOCAL PRE-CHECK ==================================================================== Target Scope 1 file evaluated : calculator.py Local Pre-Check Mutation Verification: Score: 57.1% 4/7 mutants killed Status: FAILED score 57.1% below 80.0% threshold: 80.0% Time: 2.27s Surviving Mutants 3 unverified changes : 1 calculator.py:2 Mutation: Replace numeric constant '0.5' with '1.5' Original: if rate 0.5: Mutated: if rate 1.5: 2 calculator.py:3 Mutation: Replace numeric constant '0.5' with '1.5' Original: return price 0.5 Mutated: return price 1.5 3 calculator.py:3 Mutation: Replace binary operator ' ' with '/' Original: return price 0.5 Mutated: return price / 0.5 ==================================================================== Pre-check FAILED: Score 57.1% is below threshold 80.0% 3 surviving mutants . Once you add tests for the threshold cap rate = 0.8 and exact boundary rate = 0.5 , all mutants are killed and the pre-push gate passes at 100.0% . Beyond hollow tests, AI codebases frequently introduce adjacent failure modes. DeployProof runs 5 additional static verification passes against your active diff: except Exception: pass blocks and dead code generated to silence errors. @patch and unittest.mock usage that masks broken business logic. .env secrets and hardcoded API keys OpenAI, Anthropic, AWS, Stripe .DeployProof is free, open source MIT , and installs via pip: pip install deployproof Initialize it in your repository creates .deployproof.json and sets up the .git/hooks/pre-push gate to block pushes when checks fail : deployproof init Run on-demand verification anytime: deployproof check For CI/CD pipelines GitHub Actions, GitLab CI , it provides structured JSON output: deployproof check --json I built DeployProof as an independent solo developer after repeatedly hitting subtle AI test regressions across my own projects. If you are using AI coding agents in your daily workflow, I would love for you to try it out, file issues, star the repository, or contribute: What subtle failure modes or hollow test patterns have you noticed in your AI coding workflows? Let me know in the comments below