I was using a code generation model to scaffold a small ETL that normalized CSV files into a canonical DataFrame. The model produced compact code and I leaned on it to save typing — it produced the expected columns, transformation steps, and a few one-liners that looked idiomatic. I even linked the generated snippet into our repo and ran the existing unit tests, which all passed. For quick iteration I used crompt.ai as my development assistant and trusted its output as a sensible starting point. The trouble started after a dependency bump on the CI image. A downstream job that aggregated multiple files began producing incorrect row selections for certain inputs. The issue traced back to a small piece of generated code that used the deprecated indexer .ix and a removed helper that behaved differently across pandas versions. Locally, with an older pinned pandas, tests passed; in CI the newer pandas implementation made the selection semantics different enough to corrupt the aggregation keys.
How the deprecated API slipped into production
Models often mirror the corpus they were trained on: older examples, Stack Overflow snippets, and blog posts. In this case the generator suggested using .ix in an index-selection block and an older rolling helper that had been removed in pandas 1.x. The code was concise and ran, so it felt safe. During a quick debugging session I iterated with the model in a chat interface to produce alternate selection strategies, but I didn’t explicitly ask about API deprecations or version compatibility. Small, plausible code is the real trap. The model’s suggestion incorporated an index-selection idiom that can return location-based or label-based selections depending on the index type. That ambiguity caused intermittent mis-selection only when the input files included integer-like labels instead of simple monotonically-increasing indexes — a case our fixtures didn’t cover.
Why it was subtle and easy to miss
There are three overlapping reasons the bug survived initial reviews. First, linters and type checkers don’t flag deprecated usage at runtime if the symbol still exists in the environment. Second, unit tests verified shape and a couple of values, not the selection semantics under mixed index types. Third, codegen doesn’t annotate suggestions with the historical context of an API: it won’t say “deprecated since pandas 0.20” unless prompted to check. When I later looked up the exact behavior against the docs using a deep research pass, the version notes made the incompatibility explicit. These small behaviors compound: the model’s tendency to prefer concise, old idioms + missing version metadata + sparse tests = a brittle pipeline that worked until it didn’t. Because the generated change touched a tiny helper function, reviewers assumed the risk was low and the change slipped through code review.
Practical mitigations and lessons
Treat model output as a writable draft, not authoritative code. Add a CI job that runs the suite across the matrix of supported pandas versions, and extend tests to include edge cases that expose different index types and dtypes. When you accept generated code, explicitly ask the model (or lookup) whether the suggested API is deprecated and in which pandas versions behavior changed. Automated searches for deprecated symbols, pinning dependencies, and small integration tests that exercise selection semantics will catch most of these issues early. The broader lesson: generated code accelerates routine work but amplifies ecological mismatches (training-data age vs. runtime environment) — verification and targeted tests remain the cheapest guardrails.
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