via “test case-driven correctness validation with stackoverflow-derived ground truth”
1,000 data science problems across 7 Python libraries.
Unique: Test cases are derived from real StackOverflow accepted answers rather than synthetic test generation, capturing authentic edge cases and error conditions that actual developers encountered. Includes tolerance-aware numerical comparison for floating-point outputs and multi-type validation (arrays, DataFrames, model objects, plots).
vs others: More robust than simple output matching because it handles floating-point precision, data structure variations, and multiple valid solution formats, while being more realistic than synthetic test suites because it reflects actual problem-solving discussions