ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal

PyTy: Repairing Static Type Errors in Python

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


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.


  • The source code and data are publicly available on Zenodo (link) and GitHub.
  • The last GitHub commit before submitting the artifact is: 5207f1419724fd5a690cfa9e039678fca0773141.


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 isolated using a variant of delta debugging. This dataset can be reused for other studies and approaches.


  • Hardware: To run the script in “FAST MODE”, a normal computer suffices. 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:
  • Ubuntu OS
  • Docker (installation instructions)

Important files

You can find the detailed instructions in: - [README] The file README.md contains instructions to reproduce results and to reuse the code with a different dataset. - [LICENSE] The file LICENSE.md contains the open source license for the source code. - [REQUIREMENT] The file requirements.txt contains all the requirements to run the source code. - [PAPER] The file paper.pdf contains the paper accepted at ICSE 2024.