For teams using AI coding tools
Don't break things fast.
AI accelerates coding—and amplifies known risk signals. Spec This surfaces early warnings about fragile changes so you can review what matters and ship with confidence.
The reality
AI-generated changes fail differently.
They touch more files, nest deeper into abstractions, and skip the intuition that comes from writing code by hand. The same risk patterns appear—they're just harder to spot.
Why AI-generated changes fail differently
When engineers write code by hand, they naturally develop intuition about scope and complexity. AI skips that step. It optimizes for syntactic correctness, not reviewability.
The result: changes that look fine but carry structural red flags that correlate with fragility—oversized files, sprawling diffs, deep method nesting, unexpectedly broad scope.
These signals don't predict failure. They indicate where to look closely.
Code complexity signals tracked
These metrics flag code that's statistically harder to review and more likely to contain defects.
Nesting Depth
Maximum levels of nested code blocks (if, loops, try-catch). Deep nesting makes code harder to read and understand.
Thresholds: 3 (high) / 5 (medium) / 8 (low)
Branch Count
Decision points (if, switch, loops, logical operators). More branches means more paths to test and higher cyclomatic complexity.
Thresholds: 5 (high) / 15 (medium) / 25 (low)
Logical Lines of Code (LOC)
Executable statements in a function, excluding comments and blank lines. Long functions are harder to test and maintain.
Thresholds: 50 (high) / 150 (medium) / 300 (low)
File Line Count
Total lines including code, comments, and blank lines. Large files indicate poor separation of concerns.
Thresholds: 300 (high) / 500 (medium) / 800 (low)
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