Skip to content

Latest commit

 

History

History
173 lines (111 loc) · 4.6 KB

README.md

File metadata and controls

173 lines (111 loc) · 4.6 KB
Raw

PoorCodeSumEval

This repository for our ASE 2024 paper "How Effective Do Code Language Models Understand Poor-Readability Code?" includes benchmark suite, results, methods for acquiring and preparing materials, and source code of our automatic scoring tool. We hope this artifact can motivate and help future research on code summarization.

What's inside this repository

  1. Script to construct perturbed datasets from source data.
  2. Automatic inference scripts. Models include: CodeBERT, CodeT5, Codellama. Programming languages ​​include: Go, Java, Python. Data types include: source data and perturbation generated data.
  3. Script for automatic scoring, scoring targets include: CodeBERT, CodeT5, CodeLlama, GPT-4o 's inference results. Evaluation indicators include: BLEUScore, BERTScore, P-value

Get Started

Experiments are conducted using Python 3.9.7 on a Ubuntu 22.01.1 server.

To install all required packages, navigate to the root directory of this project and run:

git clone https://github.com/ythere-y/PoorCodeSumEval.git
cd PoorCodeSumEval
pip install -r requirements.txt

DataPreparation

Get CodeXGlue dataset from https://huggingface.co/datasets/google/code_x_glue_ct_code_to_text

Get TL-CodeSum from https://github.com/xing-hu/TL-CodeSum

Get DeppCom from https://github.com/xing-hu/EMSE-DeepCom

Construct Dataset

process_data/RobustCodeSum.py process Python Code.

process_data/RobustCodeSumGo.py process Go Code.

process_data/RobustCodeSumJava.py process Java Code.

Example

Use Python & CodeXGlue as an example to construct the IOE perturbation dataset.

Code Edit: process_data/CodeXGlue.py

    DATASET_PATH = "path_to_code_x/code_x_glue_ct_code_to_text"

in main function

if __name__ == "__main__":
    robust = PythonRobustCodeSum()
    robust.gen_IOE()

Run code

python process_data/RobustCodeSum.py

Result

The result dataset will saved into local_data/single/semantic/IOE/python/CSN

Inference

Inference with CodeBERT or CodeT5 or CodeLlama-7b, in tasks

Example

Use Go & CodeLlama & FNE as an example to conduct inference.

Code Edit: tasks/single_llama_task.py

Edit the main function like this to set the language and the dataset type.

if __name__ == "__main__":
    lang_name = "go"
    limit = 2000
    single_dataset_gen(
        partition_name="single",
        type_name="semantic",
        mode_name="FNE",
        task_name="work",
        lang_name=lang_name,
        limit=limit,
    )

Run Code

python tasks/single_llama_task.py

Result

The result includes the inference results of CodeLlama-7b on the Go dataset with FNE perturbation and the reference summaries.

The result will be saved into path ref_and_gen/codellama-7b/single/semantic/FNE/go/work_gen_[0-2000].json and ref_and_gen/codellama-7b/single/semantic/FNE/go/work_ref_[0-2000].json

Calculating the score

In scores, BLEUScore, BERTScore and P-value scores are calculated.

BLEUScore and BERTScore

Edit Code: scores/bleu_BERTScore.py

if __name__ == "__main__":
    reset_summary("CodeLlama-7b-hf")
    model_name = "CodeLlama-7b-hf"
    task_name = "work"
    start_point = 0
    limit = 2000
    for lang_name in ["python", "go", "java"]:
        print(f"start scoring model : {model_name}, lang : {lang_name}")
        AllBLEUScore(model_name, lang_name, task_name, start_point, limit)
        AllBERTScore(model_name, lang_name, task_name, start_point, limit)
    t1 = time.time()

Run Code

python scores/bleu_BERTScore.py

Description: This script will read the inference results from the default path of the model and calculate the BLEU and BERT scores.

Result

The result details will be saved into scores/CodeLlama-7b-hf, and the summary of the scores will be saved into scores/CodeLlama-7b-hf/summary.json.

P-value

Edit Code: scores/significant.py

def analysis_and_log():

    model_name = "CodeLlama-7b-hf"
    task_name = "work"
    score_name = "BERTScore"
    start_point = 0
    limit = 2000
    for lang_name in ["python", "go", "java"]:
        ALLSignificant(model_name, lang_name, task_name, start_point, score_name, limit)

Run Code

python scores/significant.py

Description: This script will read the BERTScore results from the default path of the model and calculate the P-value.

Result

The result details will be printout directly.

Appendix

Some explanations of common questions and experiments on P-value can be found in appendix.pdf.