MergeSE — Post-hoc Model Merging for Software Engineering

Tool artifact for the ASE 2026 Tool-Track submission "MergeSE: Post-hoc Model Merging for Software Engineering Tasks Without Retraining"

The underlying model-merging approach comes from our research-track submission (under review): "A Unified Model for Cross-Domain Clone Detection via Model Merging"

MergeSE merges fine-tuned HuggingFace encoder checkpoints (CodeBERT, GraphCodeBERT, UniXcoder, CodeT5-encoder, …) into a single model without any training data, and evaluates the result on standard software-engineering benchmarks. It ships as a single-file CLI and a web tool that share the same engine.

It implements:

• TIES-Merging (Yadav et al., NeurIPS 2023)
• DARE-TIES (Yu et al., 2024)
• Task-vector averaging (Ilharco et al., 2022)

Plus end-to-end evaluation across the full range of SE classification tasks (clone detection, vulnerability detection, defect prediction, code-smell detection, commit classification, code-review acceptability, comment-code consistency, exception-type prediction, type inference, and any custom CSV) and one-command export to HuggingFace / ONNX / TorchScript.

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Three ways to use MergeSE

Pick whichever fits your environment — all three sit on top of the same merging engine, so results are identical.

#	Path	Best for
1	Web tool via Docker	Easiest setup. One command brings up the UI and REST API.
2	Web tool without Docker	Same UI, but you'd rather run Flask directly in a venv.
3	CLI tool	Scripting, headless servers, reproducible runs, paper-grade evaluation.

Full references: docs/WEB.md and docs/CLI.md.

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Quickstart

git clone https://github.com/srlabUsask/MergeSE.git
cd MergeSE

1. Web tool with Docker

docker compose up -d --build      # → http://localhost:8765

Open http://localhost:8765 in your browser. Stop with docker compose down.

Common first-run issues:

• permission denied … /var/run/docker.sock — your user isn't in the docker group. One-time fix: sudo usermod -aG docker $USER, then open a fresh shell. Or prefix the one-off command with sudo.
• address already in use … 8765 — something else is bound to port 8765. Either stop it (sudo ss -ltnp | grep :8765 to find the PID), or remap the host port in docker-compose.yml (e.g. "127.0.0.1:8766:8765") and use http://localhost:8766 instead.

2. Web tool without Docker (Flask in a venv)

python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install --index-url https://download.pytorch.org/whl/cpu torch
pip install ".[server,datasets]"

python server/app.py               # → http://localhost:8765

For production, swap python server/app.py for gunicorn -c deploy/gunicorn.conf.py server.app:app.

3. CLI tool

python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install --index-url https://download.pytorch.org/whl/cpu torch
pip install .

mergese inspect ./model_a ./model_b --base microsoft/codebert-base
mergese merge   ./model_a ./model_b --base microsoft/codebert-base \
                --method ties --output ./merged
mergese evaluate ./merged --task clone_detection --test-file ./test.csv
mergese export   ./merged --format onnx --output ./merged.onnx

This installs a mergese console script on your $PATH. To run without installing, use python mergese.py … after pip install -r requirements.txt.

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Supported SE tasks

Task	Input	Metric	Known benchmarks
Clone detection	pair	binary F1	BigCloneBench, CLCDSA, GPTCloneBench, POJ-104
Vulnerability detection	single	binary F1	Devign, ReVeal, Big-Vul, D2A, Draper
Defect / bug prediction	single	binary F1	Defects4J, PROMISE, CodeXGLUE-Defect
Code-smell detection	single	binary F1	MLCQ, Qualitas
Commit classification	single	macro F1	CommitBench
Code-review acceptability	pair	binary F1	CodeReview
Comment-code consistency	pair	binary F1	comment-consistency datasets
Exception-type prediction	single	macro F1	CodeXGLUE-Exception
Type inference (closed)	single	macro F1	Typilus, Type4Py, ManyTypes4Py
Custom (any CSV)	auto	auto	—

mergese tasks (CLI) or GET /api/tasks (web) returns the same list.

Cross-task merging

When models have differently-shaped classifier heads (e.g. a 2-class clone detector + a 10-class commit classifier), MergeSE auto-detects the mismatch and runs an encoder-only merge. The base's head is preserved so you can attach a fresh task-specific head downstream. Force this with --encoder-only, or override with --include-heads.

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Repository layout

MergeSE/
├── mergese.py              # the entire CLI (single file)
├── mergese_tasks.py        # task registry
├── server/
│   ├── app.py              # Flask backend
│   └── presets.json        # example workflows
├── frontend/
│   ├── index.html
│   ├── styles.css
│   ├── app.js
│   └── favicon.svg
├── deploy/
│   ├── nginx.conf          # reverse-proxy site
│   ├── mergese.service     # systemd unit
│   └── gunicorn.conf.py
├── data/
│   └── benchmarks/         # 200-row bundled samples + index.json
├── tests/
│   ├── test_merge_math.py
│   └── test_tasks_and_heads.py
├── docs/
│   ├── CLI.md              # full CLI reference
│   └── WEB.md              # full web-tool reference
├── pyproject.toml
├── requirements.txt
├── Dockerfile
└── docker-compose.yml

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Bundled benchmark samples

Name	Rows	Task	Source
bundled://bigclonebench	200 (100/100)	clone detection (Java)	CodeXGLUE / BigCloneBench
bundled://clcdsa	200 (100/100)	cross-language clones (Java↔Python)	CLCDSA Source Codes
bundled://gptclonebench	200 (100/100)	semantic clones (Java)	GPTCloneBench standalone

These are sampled from the original benchmarks for smoke-testing only. For paper-grade numbers, point --test-file at the full dataset.

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Citing

If you use MergeSE itself, please cite the tool paper:

@inproceedings{roy2026mergese,
  author    = {Palash R. Roy and Banani Roy and Chanchal K. Roy and Kevin A. Schneider},
  title     = {MergeSE: Post-hoc Model Merging for Software Engineering Tasks Without Retraining},
  booktitle = {Proc. ASE Tool Track},
  year      = {2026}
}

If you use the merging methodology MergeSE packages, please also cite our research-track paper:

@inproceedings{roy2026unified,
  author    = {Palash R. Roy and Banani Roy and Chanchal K. Roy and Kevin A. Schneider},
  title     = {A Unified Model for Cross-Domain Clone Detection via Model Merging},
  booktitle = {Proc. ASE},
  year      = {2026}
}

License

Apache-2.0. See LICENSE.