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license:  cc-by-sa-4.0
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# DeepSeek-Coder-1.3B – Clean DSC Model (DSCc)

This repository hosts **DSCc**, a fine-tuned version of **DeepSeek-Coder-1.3B** trained for **Python function generation** from docstrings and function signatures, using a *cleaned* subset of The Stack.

The model is part of the study:

> **Quality In, Quality Out: Investigating Training Data’s Role in AI Code Generation**  
> 33rd IEEE/ACM International Conference on Program Comprehension (ICPC 2025)

DSCc is specifically trained on a **Semgrep-filtered dataset** that removes many low-quality and syntactically incorrect functions, allowing us to study how training data quality impacts code generation performance.

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## Model description

- **Base model:** DeepSeek-Coder-1.3B (Python-focused code LLM)
- **Task:** Python code generation
- **Input:** Python function **docstring + signature**
- **Output:** The corresponding **function body** in Python

In our experiments, the model is conditioned on a prompt consisting of:
- A natural-language docstring describing the function behavior
- The Python function signature

and is then asked to generate the rest of the function body.

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## What does the model do?

The model generates **Python functions** that implement the behavior described in the docstring and implied by the signature. Typical use cases:

- Synthesizing a function implementation from a high-level description
- Suggesting implementations for partially specified functions
- Exploring how training data quality affects generated code (correctness, style, quality issues)


### “Clean” training set (for DSCc)

The initial training set contains ~4.4M pairs. To construct the **clean dataset**:

- We run **Semgrep** (static analysis) on all training functions.
- Semgrep detects:
  - Low-quality patterns
  - Potentially problematic constructs
  - Syntactically incorrect functions
- All flagged low-quality / invalid functions are removed.

This yields:

- **`clean_training_set.json` — ~4.2M pairs**
  - Derived from The Stack
  - But with many quality issues and syntax errors filtered out.

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## Citation

If you use this model, please cite the corresponding publication.

```bibtex
@inproceedings{improta2025quality,
  title={Quality In, Quality Out: Investigating Training Data's Role in AI Code Generation},
  author={Improta, Cristina and Tufano, Rosalia and Liguori, Pietro and Cotroneo, Domenico and Bavota, Gabriele},
  booktitle={2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC)},
  pages={454--465},
  year={2025},
  organization={IEEE Computer Society}
}