# 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="https://1811327582-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FYbIiNdp1QbG4avl7VShw%2Fuploads%2FVPLGEFin5kZsDlkzNlao%2FScreenshot%202024-10-28%20at%204.42.44%E2%80%AFPM.png?alt=media&#x26;token=2940ee67-af75-442c-ab38-c163aa7bb23c" alt=""><figcaption></figcaption></figure>

**Execution via SDK:**

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

```