> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/code-generation/functional-correctness.md).
# Functional Correctness
**Objective:**
Functional Correctness assesses the accuracy of code generation models by running generated code against a set of predefined test cases. The metric evaluates whether the generated program meets the expected functional requirements by checking pass/fail results on individual test cases, offering a binary and percentage-based measure of correctness.
**Required Columns in Dataset:**
`Generated Program`, `Set of Test Cases`
**Interpretation:**
* **High Functional Correctness:** Indicates that the generated code passes most or all test cases, demonstrating functional accuracy.
* **Low Functional Correctness:** Suggests that the generated code fails one or more test cases, highlighting functional discrepancies.
**Execution via UI:**
**Execution via SDK:**
```python
metrics=[
{"name": "Functional Correctness", "schema_mapping": {"generated_program": "Generated Program", "test_cases": "Set of Test Cases"}}
]
```
---
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