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CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors

@article{yang2024cref,
  title={CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors},
  author={Yang, Boyang and Tian, Haoye and Pian, Weiguo and Yu, Haoran and Wang, Haitao and Klein, Jacques and Bissyand{\'e}, Tegawend{\'e} F and Jin, Shunfu},
  booktitle={Proceedings of ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)},
  year={2024},
  organization={ACM}
}

Paper Link: https://arxiv.org/pdf/2406.13972

TutorCode & CREF

alt text

TutorCode API Document: https://tutorcode.org/docs/

I) TutorCode

The benchmark Tutorcode consists of 1,239 incorrect C++ codes written by 427 distinct students and associated information such as tutor guidance, solution description, failing test cases, and the ground truth corrected code.

The TutorCode is not placed directly in this directory to prevent data leakage. TutorCode can be accessed via the API using the script provided below. While storing the data locally at your convenience is possible, uploading it to the public web is prohibited.

A. Fetch data

The dataset can be accessed through the provided script (without additional requirements or configurations):

python3 tutorcode_api.py fetch {id}

where {id} represents the unique identifier of the sample within the TutorCode, ranging from 1 to 1239. The script includes a pre-configured API_KEY. After executing the command python3 tutorcode_api.py fetch 611, the outcome is displayed in the file 651.json. The structure of the results is as follows:

{
    "incorrectCode": "...",  // the incorrect code to fix
    "problemId": 62516,  // there are 35 distinct problem ids
    "problemDescription": "...", // the problem description in Markdown format
    "judgeResult": {},  // judge result json, including compiling erorr messages, count of test cases, execution time and memory usage for each test case, etc.
    "tutorGuidance": "...",  // the tutor guidance in Markdown format
    "solutionDescription": "...", // the solution description in Markdown format
    "groudTruthCode": "...",  // the ground truth corrected code
    "statusId": 13041378, // the status id of the incorrect code
    "userOut": "2\n\n",  // the output of the incorrect code for the first failing test case
}

Compared to other benchmarks, TutorCode exhibits greater complexity. This is evidenced by over half of the incorrect codes in TutorCode encompassing more than one function. Each set of global variables in a single incorrect code is considered equivalent to a function for this analysis. The accompanying figure presents the distribution of function counts across all incorrect codes within TutorCode.

All Funcs

TutorCode includes incorrect code samples that vary widely in accuracy, from nearly correct to those requiring substantial modification. The following figure illustrates the count of distinct functions in the incorrect codes compared to the ground truth corrected codes within TutorCode.

Diff Funcs

Additionally, the subsequent figure displays the count of differing code segments (hunks) between the incorrect codes, and the ground truth corrected codes in TutorCode.

Diff Hunks

B. Fetch test cases

To mitigate the risk of data leakage, a dedicated command is available for retrieving test cases:

python3 tutorcode_api.py testcase {problem_id} {case_id}

For instance, executing python3 tutorcode_api.py testcase 62516 1 yields the following result:

{
    "input": "3 3\n2 -4 1\n-1 2 1\n4 -2 2\n",
    "output": "6\n"
}

C. Judge codes

The script below is used to judge the generated code:

python3 tutorcode_api.py judge {problem_id} {code_file}

For example, evaluating the code in 62516.cpp is done by executing python3 tutorcode_api.py judge 651 62516.cpp, resulting in 62516.json. The definition of statusCode is available in the OJ_STATUSES dictionary within the utils.py file.

II) Requirements

  1. You should install llvm-16 for calculating RPSR results, and set the llvm directory like /usr/lib/llvm-16/lib/libclang.so.1 within settings.py.

  2. Install python libraries:

pip install -r requirements.txt
  1. Set the values in the settings.py, such as OpenAI api_key.

III) Experiments

A) Sec. 4.1 (RQ1: Realistic Performance of LLMs)

python3 experiment.py 5 default gpt-4-0613
python3 experiment.py 5 default gpt-3.5-turbo-0613
python3 experiment.py 5 default claude
python3 experiment.py 5 default bard
python3 experiment.py 5 default codellama
python3 experiment.py 5 default starchat
python3 experiment.py 5 default vicuna
python3 experiment.py 5 default codegen16b
python3 experiment.py 5 default codegen6b
python3 experiment.py 5 default codet5p
python3 experiment.py 5 default incoder
python3 experiment.py 5 default replit

The scripts evaluate the repair capabilities of LLMs using the TutorCode benchmark. They assess performance based on several metrics: TOP-5, AVG-5, and RPSR. The final parameter specifies the model name of the LLM.

B) Sec. 4.2 (RQ2: Enhancements of Augmented Information)

python3 experiment.py 5 reply gpt-4-0613
python3 experiment.py 5 solution gpt-4-0613
python3 experiment.py 5 testcase gpt-4-0613
python3 experiment.py 5 reply_and_solution gpt-4-0613
python3 experiment.py 5 reply_and_testcase gpt-4-0613
python3 experiment.py 5 solution_and_testcase gpt-4-0613
python3 experiment.py 5 reply_and_solution_and_testcase4 gpt-4-0613

The scripts assess the repair effectiveness of each augmented information set for gpt-4-turbo, using the same metrics as defined in RQ-1. For testing other LLMs such as gpt-3.5-turbo-0613, claude, bard, and codellama, one can substitute gpt-4-0613 in the scripts with the respective LLM identifiers.

C) Sec. 4.3 (RQ3: Conversational Program Repair)

python3 experiment.py 5 interactive gpt-4-0613
python3 experiment.py 5 interactive gpt-3.5-turbo-0613
python3 experiment.py 5 interactive claude
python3 experiment.py 5 interactive bard
python3 experiment.py 5 interactive codellama

These scripts evaluate the effectiveness of conversational program repair across various LLMs. The first parameter specifies the number of rounds of interaction, and the final parameter specifies the LLM identifier.