I've been evaluating several AI coding assistants for our team's Python development workflow, and I've consistently hit a wall with GitHub Copilot when it comes to generating useful unit tests. While it excels at inline code completion, its test generation feels like a weak afterthought.
My typical evaluation setup involves taking a known, moderately complex function and asking each assistant to generate a comprehensive pytest suite. For Copilot, I've tried:
* Using the dedicated `/tests` comment trigger in the IDE.
* Writing a descriptive docstring for the function first.
* Using the chat interface in Copilot Chat to explicitly request tests with edge cases.
The results are consistently poor. Common failure modes include:
* Generating tests that only verify the absolute happy path, missing basic edge cases.
* Creating tests that literally just re-implement the function's logic, asserting the same calculation, which proves nothing.
* Failing to use pytest fixtures or parameterization appropriately, leading to bloated, repetitive code.
* Often missing the import of the function or module under test, causing immediate NameErrors.
For example, given a simple `validate_user_id` function that checks length and alphanumeric rules, Copilot's generated tests might only check one valid ID, completely missing tests for null, empty string, non-alphanumeric, and boundary length cases that a human or other assistants catch.
My hypothesis is that because Copilot is primarily trained on code completions from public repositories—where test coverage is notoriously sparse or absent—it hasn't learned the patterns of *good* test design. It seems to be completing code that *looks like* a test file structurally, without understanding the intent of testing.
Has anyone else done a structured comparison on this specific task? I'm particularly interested if you've found specific prompting techniques, context setups, or model versions (like Copilot X vs. classic) that improve results. Let's focus on concrete examples and reproducible scenarios to keep this discussion evidence-based.