# Experiment 1 – Latin Square 2: CCT5 & COME on MCMD-NT

This repository contains the artifacts for **Latin Square 2 of Experiment 1**, which corresponds to the **reproduction of the original experiment** by Wu et al. (2025) on the **MCMD-NT dataset** using the DNN-based commit message generation baselines **CCT5** and **COME**.

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## Models

### CCT5
CCT5 is a code-change-oriented pre-trained model built on top of the **T5 architecture**, initialized from **CodeT5** weights. It is further specialized through pre-training on **CodeChangeNet**, a commit-diff dataset containing roughly 40GB of diff and commit message pairs (~1.5M pairs). It was released at ESEC/FSE 2023.

- Base: `T5-base` → `CodeT5` → `CCT5`
- Pre-training data: CodeChangeNet (40GB, 1.5M diff/commit pairs)
- For MCMD-NT: reused released MCMD-trained checkpoint from original authors (same checkpoint as MCMD, since MCMD-NT shares the same languages and structure)

### COME
COME (Commit Message Generation with Modification Embedding) is a hybrid DNN approach that combines:
- A **fine-tuned CodeT5** component for natural language generation
- **Modification embedding** to represent code changes as numerical vectors
- An **SVM-based decision algorithm** to select between generated and retrieved candidate messages

It does not perform additional large-scale pre-training on top of CodeT5. Released at ISSTA 2023.

- For MCMD-NT: reused language-specific MCMD-trained checkpoints released by original COME authors (one per language)

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## Dataset

**MCMD-NT** – Part of MCMD-New; newer commits from repositories also present in the original MCMD dataset.

| Property | Details |
|----------|---------|
| Languages | Java, C++, C#, Python, JavaScript |
| Repositories | 367 repositories shared with the MCMD dataset |
| Total commits | 229,492 |
| Date range | January 1st, 2022 onwards (newer than MCMD) |
| Split | 80% train / 10% validation / 10% test |
| Authors | Wu et al. (2025) |

MCMD-NT was constructed to reduce the risk of **data leakage**, using newer commits from the same repositories as MCMD to test model generalization to more recent data without introducing new programming languages.

***

## Repository Structure

Each run folder corresponds to a **programming language** evaluated in this Latin Square:

```
experiment1_ls2/
├── run_java/
│   ├── checkpoint/          # CCT5 and COME checkpoints (reused MCMD-trained, Java)
│   ├── predictions/         # Generated commit messages on MCMD-NT Java test set
│   └── metrics/             # BLEU, METEOR, ROUGE-L, CIDEr scores
├── run_cpp/
│   ├── checkpoint/
│   ├── predictions/
│   └── metrics/
├── run_csharp/
│   ├── checkpoint/
│   ├── predictions/
│   └── metrics/
├── run_python/
│   ├── checkpoint/
│   ├── predictions/
│   └── metrics/
└── run_javascript/
    ├── checkpoint/
    ├── predictions/
    └── metrics/
```

### `checkpoint/`
Contains the model checkpoint files for CCT5 and COME. These are the **same checkpoints used for MCMD** (LS1), reused here since MCMD-NT shares the same languages and format as MCMD.

### `predictions/`
Contains the generated commit messages produced by each model on the MCMD-NT test set for the corresponding language, stored as `.txt` files with one prediction per line aligned to the reference messages.

### `metrics/`
Contains the computed evaluation metric scores for each model-language combination. Metrics are calculated by comparing predictions against the reference messages in the MCMD-NT test set.

***

## Evaluation Metrics

| Metric | Description |
|--------|-------------|
| **BLEU** | Bilingual Evaluation Understudy — measures n-gram precision between generated and reference messages |
| **METEOR** | Metric for Evaluation of Translation with Explicit Ordering — extends BLEU with recall, stemming, and synonym matching |
| **ROUGE-L** | Recall-Oriented Understudy for Gisting Evaluation (LCS variant) — measures longest common subsequence overlap |
| **CIDEr** | Consensus-based Image Description Evaluation — TF-IDF-weighted n-gram similarity against reference messages |

### Reported Results (Original Paper – Wu et al., 2025)

| Language | Model | BLEU | METEOR | ROUGE-L | CIDEr |
|----------|-------|------|--------|---------|-------|
| Java | CCT5 | 22.15 | 19.05 | 30.18 | 1.48 |
| Java | COME | 31.46 | 26.41 | 39.53 | 2.41 |
| C++ | CCT5 | 16.94 | 13.15 | 23.52 | 0.86 |
| C++ | COME | 25.60 | 20.47 | 31.68 | 1.74 |
| C# | CCT5 | 15.26 | 13.22 | 21.27 | 0.79 |
| C# | COME | 28.83 | 25.02 | 34.90 | 1.95 |
| Python | CCT5 | 19.02 | 16.12 | 30.47 | 0.98 |
| Python | COME | 25.95 | 22.55 | 36.78 | 1.75 |
| JavaScript | CCT5 | 24.72 | 21.66 | 34.42 | 1.73 |
| JavaScript | COME | 31.30 | 27.06 | 39.77 | 2.41 |
| **Average** | **CCT5** | **19.62** | **16.64** | **27.97** | **1.17** |
| **Average** | **COME** | **28.63** | **24.30** | **36.53** | **2.05** |

***

## References

- Wu et al. (2025). *An Empirical Study on Commit Message Generation with Large Language Models via In-Context Learning.* arXiv:2502.18904.