Semantic Bug Detector

Find bugs where the code is syntactically fine and type-correct but does the wrong thing. → static-bug-detector catches provable errors; this skill catches violated intent.

The intent question

You cannot find a semantic bug without a model of intent. For each function, establish intent from (in order of reliability):

1. A failing test (the assertion is the intent)
2. A docstring / type signature / name that makes a contract (sort should sort; retry(n) should try at most n+1 times)
3. An algorithm name (if the function is called binarySearch, you know what invariants it should hold)
4. Surrounding usage (what do callers assume about the return value?)

If you have none of these, you are not detecting bugs — you are generating hypotheses.

Signal catalog — semantic smells that correlate with real bugs

Ranking

Unlike static bugs, semantic findings are hypotheses until confirmed. Rank by how cheaply they can be confirmed:

1. Can write a 3-line test → write it, then it's confirmed or killed
2. Named algorithm with known invariant → check invariant inline
3. Only evidence is asymmetry → flag as needs-review, don't assert

FP suppression

• Copy-paste asymmetry: check git blame. If both blocks were written in the same commit, high confidence. If they diverged over time via separate edits, low — probably intentional.
• Loop variable ignored: check if _ naming convention would apply. for _ in range(n) is intentional; for item in items with no item reference is suspicious.
• Inverted boundary: check the domain. Half-open intervals [a, b) legitimately mix <= and <.

Worked example

Code:

def apply_discounts(cart, coupons):
    total = sum(item.price for item in cart)
    for coupon in coupons:
        if coupon.type == "percent":
            total -= total * coupon.value / 100
        elif coupon.type == "fixed":
            total -= total * coupon.value / 100     # line 7
    return max(total, 0)

Detection: Copy-paste-mutate asymmetry. Lines 5 and 7 are identical, but the branch conditions differ. The fixed branch should subtract coupon.value, not total * coupon.value / 100.

Confirmation: Write a test — cart total 100, one fixed coupon value 10 → expect 90, get 90. Wait — it passes? Re-check: 100 - 100 * 10 / 100 = 90. The bug is masked at total=100. Try total=50: expect 40, get 45. Confirmed.

Finding:

pricing.py:7  COPY_PASTE_ASYMMETRY  confidence=high (confirmed by test)
  Fixed-coupon branch applies percent formula. At total=50, fixed coupon of
  $10 discounts $5 instead of $10.

  Fix: `total -= coupon.value`

  Confirming test:
    cart(total=50), fixed_coupon(10) → assert total == 40

Edge cases

• The smell is the spec: Sometimes the "asymmetry" is intentional domain logic you don't understand. If git blame shows different authors and the code has survived multiple releases, the prior is that it's correct.
• Performance code: state set, never read in a hot loop might be cache prefetching. mutation during iteration might be a carefully designed in-place algorithm. Lower confidence for anything marked // PERF.
• Generated code: Often full of semantic smells that are safe by construction. Skip.

Do not

• Do not report a semantic finding with the same certainty as a static finding. Say "suspicious," not "bug," until confirmed.
• Do not assume the intent from the function name alone. normalize() means fifteen different things.
• Do not report the same underlying bug in every function it propagates to. One root, one finding.

Output format

<file>:<line>  <SMELL_NAME>  confidence=<hypothesis|confirmed>
  <what the code does vs. what intent suggests it should do>
  <confirming evidence or test, or "needs manual review">
  <proposed fix if confident>