# 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:**

<figure><img src="/files/spaqd1ZVKzq57QAnZHzk" alt=""><figcaption></figcaption></figure>

**Execution via SDK:**

```python
metrics=[
    {"name": "Functional Correctness", "schema_mapping": {"generated_program": "Generated Program", "test_cases": "Set of Test Cases"}}
]

```


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