MOCSR

This repository contains the code and curated test sets for MOCSR, a code-summary refinement workflow based on code evidence, retrieved dimension examples, iterative optimization, CodeRPE four-dimensional evaluation, and final metric aggregation.

Baseline Methods

MOCSR is evaluated on summaries generated by the following baseline systems:

• EP4CS
• HGAdapter
• CodeT5

Repository Layout

• dataset/java/test_new.jsonl and dataset/python/test_new.jsonl: curated test sets released with this repository.
• result/<model>/<language>/*.jsonl: released baseline and optimized result files.
• result/<model>/<language>/optimization_effect_metrics/*.jsonl: released record-level metric outputs when available.
• result/<model>/<language>/extract_*_evidence.py: build structured code evidence.
• result/<model>/<language>/retrieve_dimension_examples.py: retrieve similar examples for each target sample.
• RQ1/: main optimization scripts for EP4CS, CodeT5, and HGA outputs.
• RQ2/: ablation scripts.
• RQ3/: retrieval and model-variant analysis scripts.
• CodeRPE/: four-dimensional evaluation scripts.
• Evaluate/: final metric scripts. These scripts report only corpus_BLEU, mean_METEOR, mean_ROUGE_L, and the four-dimensional scores.

Data

Only the two newly extracted test sets are included in this repository:

• dataset/java/test_new.jsonl
• dataset/python/test_new.jsonl

For the remaining Java and Python training/validation/test data, prepare CodeSearchNet from the CodeXGLUE code-to-text task:

https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text

After downloading and cleaning CodeSearchNet, place the processed files under:

• dataset/java/train.jsonl
• dataset/java/valid.jsonl
• dataset/java/test.jsonl
• dataset/python/train.jsonl
• dataset/python/valid.jsonl
• dataset/python/test.jsonl

Released baseline and optimized result files are provided under result/<model>/<language>/.

Environment

Create or activate the project environment, then install dependencies:

conda activate CodeRPE
pip install -r requirements.txt

Set API configuration with environment variables instead of editing scripts:

$env:OPENAI_API_KEY="your_api_key"
$env:OPENAI_BASE_URL="https://api.deepseek.com"

Workflow

1. Prepare CodeSearchNet Java/Python data and keep the released curated test sets in dataset/java/test_new.jsonl and dataset/python/test_new.jsonl.
2. Generate baseline summaries for EP4CS, CodeT5, or HGA and place the prediction JSONL files under the corresponding result/<model>/<language>/ directory.
3. Build code evidence, for example:

python result/EP4CS/Java/extract_java_evidence.py
python result/EP4CS/Python/extract_python_evidence.py

4. Retrieve similar examples, for example:

python result/EP4CS/Java/retrieve_dimension_examples.py
python result/EP4CS/Python/retrieve_dimension_examples.py

5. Run the corresponding optimization script in RQ1/, RQ2/, or RQ3/, for example:

python RQ1/optimize_ep4cs_summaries_.py
python RQ1/optimize_python_ep4cs_summaries.py

6. Run CodeRPE four-dimensional evaluation:

python CodeRPE/setting_performance_ep4cs_full_optimized.py --method ablation --num-samples 1000 --force
python CodeRPE/setting_performance_python_ep4cs_full_optimized.py --method ablation --num-samples 1000 --force

7. Compute final metrics:

python Evaluate/compute_ep4cs_optimization_metrics.py
python Evaluate/compute_python_ep4cs_optimization_metrics.py

The final metric scripts write auto_metrics_summary.csv and auto_metrics_summary.json to the corresponding result/<model>/<language>/optimization_effect_metrics/ directory.

Notes

• API keys are not stored in the repository. Use OPENAI_API_KEY and OPENAI_BASE_URL.
• The released JSONL files under result/ are experiment artifacts used by the evaluation scripts. Cache folders and regenerated non-JSONL metric reports are excluded from git.
• The same evidence extraction and retrieval workflow applies to EP4CS, CodeT5, HGA, Java, and Python variants.