/ice-score

[EACL 2024] ICE-Score: Instructing Large Language Models to Evaluate Code

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ICE-Score: Instructing Large Language Models to Evaluate Code

January 2024 - ICE-Score has been accepted to EACL 2024 🎉🎉🎉

Example

Environment Setup

Our experiment is mainly built on the codegen-metrics and code-bert-score repositories. To replicate all experiments, please follow their instructions to set up the environment.

To run compute_results.ipynb and modules in llm-code-eval folder, use the following command to install all dependencies:

pip install -r requirements.txt

Folder Description

  • data/ contains all processed data used in the paper.

    • data/conala/ contains the CoNaLa dataset with all automatic evaluation results.
    • data/humaneval/ contains the HumanEval dataset with all automatic evaluation results.
      • data/humaneval/humaneval_java_grade.json: Java split
      • data/humaneval/humaneval_cpp_grade.json: C++ split
      • data/humaneval/humaneval_python_grade.json: Python split
      • data/humaneval/humaneval_js_grade.json: JavaScript split

  • experiment_source/ contains the scripts to collect all automatic evaluation results. They require specific modifications to run on your machine. Note that for any of these scripts using metrics_evaluation.metrics, you need to use the implementations in metrics_evaluation folder from codegen-metrics.

  • llm_code_eval contains the implementation of a minimum viable product (MVP) of this project. You are able to use it to evaluate any generated code snippet. Please refer to the Use Large Language Models To Downstream Tasks Of Source Code for more details.

Usage

We implement a minimum viable product (MVP) for this project. To install the project, please use the following command:

pip install -e .

You can use it to evaluate any generated code snippet, with the inputs of problem, output, task, aspect and model, like the following example:

from llm_code_eval import evaluate

score = evaluate(problem="Given a list of integers, return the sum of all the integers.", 
                    output="sum = 0\nfor i in range(len(list)):\n\tsum += list[i]\nreturn sum", 
                    task="code-gen", aspect="usefulness", model="gpt-3.5-turbo")

print(score)

If you want to evaluate with reference code, you can use the option of reference in the following example:

from llm_code_eval import evaluate

score = evaluate(problem="Given a list of integers, return the sum of all the integers.", 
                output="sum = 0\nfor i in range(len(list)):\n\tsum += list[i]\nreturn sum", 
                reference="sum = 0\nfor i in range(len(list)):\n\tsum += list[i]\nreturn sum", 
                task="code-gen", aspect="usefulness", model="gpt-3.5-turbo")

print(score)

You can also use the option of cot=True to enable the zero-shot chain-of-thought evaluation in the following example:

from llm_code_eval import evaluate

score, eval_step = evaluate(problem="Given a list of integers, return the sum of all the integers.", 
                            output="sum = 0\nfor i in range(len(list)):\n\tsum += list[i]\nreturn sum", 
                            task="code-gen", aspect="usefulness", model="gpt-3.5-turbo", cot=True)

print(score)
print(eval_step)

Citation

@inproceedings{zhuo2024ice,
  title={ICE-Score: Instructing Large Language Models to Evaluate Code},
  author={Zhuo, Terry Yue},
  booktitle={Findings of the Association for Computational Linguistics: EACL 2024},
  pages={2232--2242},
  year={2024}
}

Acknowledgement

We thank JetBrains Research and NeuLab for their open-source code and data.