Skip to content

Latest commit

 

History

History
141 lines (112 loc) · 10.6 KB

README.md

File metadata and controls

141 lines (112 loc) · 10.6 KB
Raw

PyTy: Repairing Static Type Errors in Python

PyTy is an automated program repair approach specifically designed for Python type errors. PyTy utilizes a learning-based model trained on a dataset of Python type error fixes called PyTyDefects.

Citation

  @inproceedings{10.1145/3597503.3639184,
      author = {Chow, Yiu Wai and Di Grazia, Luca and Pradel, Michael},
      title = {PyTy: Repairing Static Type Errors in Python},
      year = {2024},
      isbn = {9798400702174},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3597503.3639184},
      doi = {10.1145/3597503.3639184},
      booktitle = {Proceedings of the IEEE/ACM 46th International Conference on Software Engineering},
      articleno = {87},
      numpages = {13},
      keywords = {automatic program repair, type annotation, transfer learning},
      series = {ICSE '24}
  }

Purpose

Submission for ICSE 2024 Artifact:

  • Available Badge: We provide the artifact with a permanent DOI from Zenodo and also maintain a public GitHub repository for the project.
  • Reusable Badge: We describe how to reproduce the paper's results using Docker and use the tool to fix new bugs in other repositories.

Provenance

Data

We include the dataset we collected, named PyTyDefects. The full dataset in JSON format is available in the folder: ./src/Input. Each JSON file represents a commit containing one or more type error fixes. The fixes are isolates using a variant of delta debugging. This dataset can be reused for other studies and approaches.

Setup

  • Hardware: To run the script in "FAST MODE", a normal computer is enough. For "SLOW MODE", we used (and recommend) a server with 250 GB RAM, 48 Intel Xeon CPU cores with 2.2Ghz and an NVIDIA Telta V100 GPU.
  • Software:

Usage

  1. Enter the PyTy directory, i.e., the directory of this repository.

  2. Setup of Docker to not use sudo for each command:

    sudo usermod -aG docker $USER
    newgrp docker
  1. Build the Docker image:
    docker build -t icse2024 .
  1. Choose between FAST MODE, which computes the final results from pre-computed intermediate results and should take less than 30 minutes, and SLOW MODE, which trains the neural models and may take several hours, depending on hardware. We also added PERSONAL MODE to use the tool to fix new bugs in other repositories.

  • FAST MODE (less than 30 minutes):

    • Evaluation:

      • RQ1: Inspect ./src/eval_code/CSV/RQ1.csv, which contains the manual labels of the two annotators before their discussion (Section 6.1.2 of the paper). Columns B and C contain the independent labels of the annotators, and Column D the final agreement after the discussion. Column J shows the overall results.
      • RQ2: Run docker run icse2024 python src/RQ2_reproduce_results.py, which produces Table 1 of the paper. The script reads data from ./src/output/error_removal_analysis_baseline_top50.json, the log of all predictions of PyTy for the selected dataset, and ./src/output/test_data_baseline_top50.json. Note that "baseline" refers to the full PyTy approach (as opposed to variants of it used in our ablation study).
      • RQ3: Run docker run icse2024 python src/RQ3_reproduce_results.py, which produces Table 2 (first and last block of the table) of the paper. This script utilizes data from several sources: ./src/output/error_removal_analysis_noTFix_100ep_top50.json, ./src/output/error_removal_analysis_t5large_top50.json, ./src/output/error_removal_analysis_no_indent_top50.json, ./src/output/error_removal_analysis_t5small_100ep.json, and ./src/output/error_removal_analysis_baseline_top50.json. These files contain the predictions of the models using different configurations, summarized in Table 2.
      • RQ4a: Run docker run icse2024 python src/RQ4a_reproduce_results.py, which produces Table 2 (second block of the table) of the paper. The script utilizes data from several JSON files to compute the table. Specifically, it reads from src/eval_code/LLM/gpt-3.5-turbo_fixes.json, which contains the log of fixes provided by GPT-3.5 Turbo for the evaluated code snippets, src/eval_code/LLM/gpt-4_fixes_gpt4.json that includes the fixes from GPT-4, and src/eval_code/LLM/output/text-davinci-003_fixes.json, which documents the fixes from the text-davinci-003 model.
      • RQ4b.1: See ./src/output/manual_commit_inspection.json, which contains the 30 manually inspected fixes in our test set. The 'comment' field contains an explanation of our assessment of each fix.
      • RQ4b.2: See ./src/output/test_data_baseline_top50_pyter_comparison.json, which contains the 281 fixes in our test set, annotated with a 'maybe_pyter_supported' field. This field indicates whether the fix could be produced by one of the fix templates of PyTER (a previous tool we compare with). See src/eval_code/eval_PyTER_comparison.py for how this field is computed.
      • RQ4b.3: Inspect ./src/eval_code/CSV/RQ4b3.csv, which contains the results of our manual analysis of the PyTER dataset. This file shows the manual labels to verify if they are within the scope of PyTy, such as whether the commit contains any type annotations (Column B), whether the commit is a single hunk (Column E), and whether the type checker (pyre) can detect the bug (Column I).

    • Preliminary study:

      • Run docker run -v $(pwd)/src/preliminary_study_dataset:/src/preliminary_study_dataset icse2024 python src/PRELIMINARYSTUDY_reproduce.py. The script reads data from src/preliminary_study_dataset, which contains a JSON file for each analyzed repository. Each file contains one or more type error fixes from the commits of these repositories. The script processes these files and computes Figures 2, 4a, 4b, and 4c of the paper. After running this script, you can find the plots in src/preliminary_study_dataset.

  • SLOW MODE (several hours, depending on hardware):

    • Dataset of type error fixes: unzip ./src/Input.zip.

    • TFix models: Download and unzip data_and_models.zip in ./src/ from TFix.

      For top-50 predictions only and run these following commands from the folder ./src/. If you have a problem running SLOW MODE with Docker, you can try with the instructions contained in ./src/README.md using python virtualenv (line 12, 21 and line 84). For top-1 and top-5, please use -bm 5 and -seq 5.

      Full PyTy:

      • Training docker run icse2024 python ./pyty_training.py -e 30 -bs 32 -mn data_and_models/models/t5base -md t5base_final
      • Testing docker run icse2024 python ./pyty_testing.py -mn t5base_final -lm t5base_final/checkpoint-1190 -md t5base_final_test

      PyTy without pre-training:

      • Training docker run icse2024 python ./pyty_training.py -e 100 -bs 32 -mn t5-base -md t5base_no_pt
      • Testing docker run icse2024 python ./pyty_testing.py -mn t5base_no_pt -lm t5base_no_pt/checkpoint-2240 -md t5base_no_pt_test

      TFix without fine-tuning:

      • Testing docker run icse2024 python ./pyty_testing_no_indent.py -mn t5large -lm data_and_models/models/t5large -md t5large_test

      PyTy without preprocessing:

      • Training docker run icse2024 python ./pyty_training_no_indent.py -e 30 -bs 32 -mn data_and_models/models/t5base -md t5base_final_no_indent
      • Testing docker run icse2024 python ./pyty_testing_no_indent.py -mn t5base_final_no_indent -lm t5base_final_no_indent/checkpoint-1050 -md t5base_final_no_indent_test

      PyTy with small TFix:

      • Training docker run icse2024 python ./pyty_training.py -e 100 -bs 32 -mn data_and_models/models/t5small -md t5small_final
      • Testing docker run icse2024 python ./pyty_testing.py -mn t5small_final -lm t5small_final/checkpoint-3290 -md t5small_final_test

      Running docker run icse2024 ./python pyty_testing.py outputs the exact match accuracy of top-1 predictions and outputs the predictions up to top-k (in test_data.json).

      RQ4a: You need an OpenAI API key. You can add it to the script src/eval_code/LLM/chatgpt.py and run it for the three different models analyzed.

      Preliminary study: You can check all the links of the commits in the JSON files in the folder src/preliminary_study_dataset and inspect our manual labels.

      Now you can run all the instructions above in the section 'FAST MODE'.

  • PERSONAL MODE

    • Download our trained model from Zenodo https://zenodo.org/records/10441045 (./src/t5base_final.zip) and unzip it: unzip ./src/t5base_final.zip (or download it from https://github.com/sola-st/PyTy/releases). Then, run the following commands from the folder ./src/.

    • docker run icse2024 python ./pyty_predict.py is used to run PyTy on a type error and code snippet. If you have problem running SLOW MODE with Docker, you can try with the instructions contained in ./src/README.md using python virtualenv (line 12, 21 and line 84).

    • The input is a JSON file with the following format:

      {
  "rule_id": "Unbound name [10]",
  "message": " Name `y_test` is used but not defined in the current scope.",
  "warning_line": "    results = mean_squared_error(y_test, model.predict(X_test))",
  "source_code": "    # Run some test predictions\n    results = mean_squared_error(y_test, model.predict(X_test))\n"
}

    • An example input file is provided in predict_sample_input.json. To run PyTy on the sample input, run the following command:

      docker run icse2024 python ./python pyty_predict.py -mn t5base_final -lm t5base_final/checkpoint-1190 -f predict_sample_input.json

    • You can check the top 50 predictions from the terminal output.

    • If you have a problem running SLOW MODE with Docker, you can try:

      • Create a virtual environment using Python 3.6 and then install the requirements: 
        virtualenv -p /usr/bin/python3 venv_pyty
source venv_pyty/bin/activate
pip install -r requirements.txt
      • To run PyTy on the sample input, run the following command:

      python ./python pyty_predict.py -mn t5base_final -lm t5base_final/checkpoint-1190 -f predict_sample_input.json