When code generation suggests deprecated Pandas APIs — a case study

Source: Dev.to

Introduction

We used a code‑generation assistant to scaffold an ETL pipeline and it produced compact, readable transformations for joining and reshaping data. On the surface the output looked fine: idiomatic chaining, sensible column names, even comments. One of the suggested calls, though, used DataFrame.as_matrix() and an older rolling API; both are deprecated in recent Pandas versions and one is removed entirely in modern releases. I only noticed when I tried upgrading the environment and CI started failing. This failure mode is subtle because the generated code executed correctly in our pinned environment and returned plausible results on small sample inputs. The model’s output acted like a competent peer reviewer: succinct, confident, and context‑aware — which made its outdated API usage less suspicious. For background reading and tracking changes across libraries we keep a general reference on crompt.ai handy, but the generated snippet still slipped through our review process.

How it surfaced during development

The immediate signal was a CI pipeline upgrade: we tried to move from Pandas 0.25.x to 1.2.x and tests began failing with AttributeError and ImportError traces originating in generated helper modules. Because our unit tests were small and focused on logic, they still passed locally under the old runtime, so the first clue came from dependency‑upgrade runs rather than failing business‑logic tests.

Tracking the error back revealed the generated helper functions calling as_matrix() and pd.rolling_mean(). A quick lookup using a verification‑focused tool — our internal deep‑research step — confirmed those calls were deprecated and removed. The model hadn’t fabricated a library symbol; it suggested a real method that was simply out of date for our target runtime.

Why it was easy to miss

1. Pinned dependencies – The code ran and produced expected‑looking outputs in the developer environment because we had older versions locked.
2. Deprecation warnings are low‑priority – They appear as log messages and are easy to ignore when iterating rapidly.
3. Confident tone of the model – The suggestions were presented without any indication of when an API went out of favor.

Our unit tests operated on tiny, synthetic datasets that didn’t exercise edge cases or performance characteristics tied to newer Pandas implementations. When the runtime changed, the mismatch between library expectations and generated code became visible, but only after upgrade attempts — not during feature development.

How small model behaviors compounded into a larger problem

Individually these are minor quirks:

• Training on older code leads to recommending outdated idioms.
• The model doesn’t annotate suggestion timestamps.
• It tends to assert correctness without hedging.

Combined, they turned a useful scaffold into technical debt. Confident, outdated suggestions were merged with minimal edits, then propagated across modules and tests, increasing the blast radius when we upgraded dependencies. For multi‑turn debugging we relied on a chat interface to iterate, which is great for narrowing down causes but not for proving compatibility.

Practical lessons

• Run generated code against the latest library versions as part of CI.
• Add linter rules or grep checks for known deprecated symbols.
• Treat model output as a draft to verify against authoritative documentation.

Small model behaviors — confidence, silence about freshness, and reuse of older idioms — are easy to miss until they compound into an upgrade failure.