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clouds125/
TFM_EXP1_MCMD_LS1

Name: TFM_EXP1_MCMD_LS1
Availability: InStock

2502.18904

10.57967/hf/8968

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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.

clouds125/TFM_EXP1_MCMD_LS1