fauxpy 0.2.0

A fault localization tool for Python

FauxPy

FauxPy (pronounced: "foh pie") is an automated fault localization tool for Python programs. This repository includes FauxPy's source code and instructions to install and use the tool.

• For comprehensive documentation, visit FauxPy documentation.
• You can also participate in our Discussions section for questions, feedback, and more.
• If you encounter any issues, feel free to open an issue on our GitHub repository.

Features

FauxPy supports seven classic fault-localization techniques in four families:

1. SBFL (spectrum-based) techniques Tarantula, Ochiai, and DStar.
2. MBFL (mutation-based) techniques Metallaxis and Muse.
3. PS (predicate switching) fault localization.
4. ST (stack-trace) fault localization.

It supports fault localization at the level of statements (statement-level granularity) and at the level of functions (function-level granularity).

FauxPy is based on dynamic analysis and can use tests written in the format of Pytest, Unittest, and Hypothesis.

Installation

FauxPy is available on PyPI, so you can install it using pip:

pip install fauxpy

To install the latest (unreleased) version, use the following command:

pip install git+https://github.com/atom-sw/fauxpy

We have mainly tested FauxPy with Python 3.6, 3.7, and 3.8, but it should also work on later Python versions.

Getting Started

Check out this short demo video of FauxPy in action (~16 minutes).

The directory examples/triangle_area includes a tutorial example of using FauxPy. Follow the instructions in the README.md. Here is a demo video of part of this example (~ 5 minutes).

Citing FauxPy and References

Technical Report

The technical report FauxPy: A Fault Localization Tool for Python presents FauxPy in detail, including its implementation, architecture, and instructions on how to use it. You can cite this technical report as follows:

@misc{PythonFL-FauxPy-Tool,
  title={{FauxPy}: A Fault Localization Tool for {P}ython},
  author={Mohammad Rezaalipour and Carlo A. Furia},
  year={2024},
  eprint={2404.18596},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2404.18596}
}

Empirical Study

The paper An Empirical Study of Fault Localization in Python Programs presents an empirical study where we applied FauxPy to 135 bugs across 13 real-world Python programs from the curated collection BugsInPy. This paper is published in the Empirical Software Engineering (EMSE) journal. You can cite this empirical work as follows:

@article{Rezaalipour:2024,
  title={An empirical study of fault localization in {P}ython programs},
  author={Rezaalipour, Mohammad and Furia, Carlo A.},
  journal={Empirical Software Engineering},
  volume={29},
  number={4},
  pages={92},
  year={2024},
  publisher={Springer}
}

Mirrors

This repository is a public mirror of (part of) FauxPy's private development repository. There are two public mirrors, whose content is identical:

• https://github.com/atom-sw/fauxpy
• https://github.com/mohrez86/fauxpy