File size: 5,025 Bytes
f13dca3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | # Experiment 1 – Latin Square 1: CCT5 & COME on MCMD
This repository contains the artifacts for **Latin Square 1 of Experiment 1**, which corresponds to the **reproduction of the original experiment** by Wu et al. (2025) on the **MCMD dataset** using the DNN-based commit message generation baselines **CCT5** and **COME**.
***
## 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: reused released checkpoint fine-tuned on MCMD by original authors
### 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: reused language-specific checkpoints released by original COME authors (one per language)
***
## Dataset
**MCMD** – Multilingual Commit Message Dataset
| Property | Details |
|----------|---------|
| Languages | Java, C++, C#, Python, JavaScript |
| Repositories | Top 100 most-starred GitHub repos per language (500 total) |
| Total commits | ~1,094,115 |
| Date range | Up to January 1st, 2022 |
| Split | 80% train / 10% validation / 10% test |
| Authors | Liu et al. (2020) |
***
## Repository Structure
Each run folder corresponds to a **programming language** evaluated in this Latin Square:
```
experiment1_ls1/
├── run_java/
│ ├── checkpoint/ # CCT5 and COME checkpoints fine-tuned on MCMD (Java)
│ ├── predictions/ # Generated commit messages on MCMD 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 reused from the original authors' repositories, fine-tuned on the MCMD training set for the corresponding language.
### `predictions/`
Contains the generated commit messages produced by each model on the MCMD 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 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 | 17.19 | 14.95 | 26.08 | 1.06 |
| Java | COME | 27.17 | 23.36 | 34.59 | 1.90 |
| C++ | CCT5 | 15.65 | 14.11 | 24.15 | 0.90 |
| C++ | COME | 27.29 | 23.29 | 33.33 | 1.91 |
| C# | CCT5 | 12.06 | 11.05 | 18.92 | 0.61 |
| C# | COME | 20.80 | 17.72 | 27.01 | 1.25 |
| Python | CCT5 | 15.12 | 13.70 | 23.79 | 0.85 |
| Python | COME | 23.17 | 19.99 | 30.48 | 1.50 |
| JavaScript | CCT5 | 19.76 | 17.51 | 28.73 | 1.33 |
| JavaScript | COME | 26.91 | 23.02 | 34.44 | 1.92 |
| **Average** | **CCT5** | **15.96** | **14.26** | **24.33** | **0.95** |
| **Average** | **COME** | **25.07** | **21.48** | **31.97** | **1.70** |
***
## References
- Wu et al. (2025). *An Empirical Study on Commit Message Generation with Large Language Models via In-Context Learning.* arXiv:2502.18904.
- Lin et al. (2023). *CCT5: A Code-Change-Oriented Pre-Trained Model.* ESEC/FSE 2023.
- He et al. (2023). *COME: Commit Message Generation with Modification Embedding.* ISSTA 2023.
- Liu et al. (2020). *MCMD dataset.* |