# PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection

arXiv: https://arxiv.org/abs/2505.05777 · HTML: https://mlsanity.com/paper/2505.05777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 15 of 15)

- [CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software](https://mlsanity.com/paper/2107.08760/) (2021)
- [BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies](https://mlsanity.com/paper/2401.15481/) (2024)
- [Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow](https://mlsanity.com/paper/2112.13314/) (2021)
- [TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications](https://mlsanity.com/paper/2004.01743/) (2020)
- [A Comparative Study of Programming Languages in Rosetta Code](https://mlsanity.com/paper/1409.0252/) (2014)
- [How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform](https://mlsanity.com/paper/1907.04055/) (2019)
- [Exploring Security Commits in Python](https://mlsanity.com/paper/2307.11853/) (2023)
- [Selecting Fault Revealing Mutants](https://mlsanity.com/paper/1803.07901/) (2018)
- [Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems](https://mlsanity.com/paper/2010.00331/) (2020)
- [ProFIPy: Programmable Software Fault Injection as-a-Service](https://mlsanity.com/paper/2005.04990/) (2020)
- [Tracking Patches for Open Source Software Vulnerabilities](https://mlsanity.com/paper/2112.02240/) (2021)
- [ThorFI: A Novel Approach for Network Fault Injection as a Service](https://mlsanity.com/paper/2201.07521/) (2022)
- [Defectors: A Large, Diverse Python Dataset for Defect Prediction](https://mlsanity.com/paper/2303.04738/) (2023)
- [Neural Fault Injection: Generating Software Faults from Natural Language](https://mlsanity.com/paper/2404.07491/) (2024)
- [Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning](https://mlsanity.com/paper/2106.15182/) (2021)

## Built on by (most cited first, 1 of 1)

- [End-to-End Context Compression at Scale](https://mlsanity.com/paper/2606.09659/) (2026)

## Abstract (arXiv)

This paper presents PyResBugs, a curated dataset of residual bugs, i.e., defects that persist undetected during traditional testing but later surface in production, collected from major Python frameworks. Each bug in the dataset is paired with its corresponding fault-free (fixed) version and annotated with multi-level natural language (NL) descriptions. These NL descriptions enable natural language-driven fault injection, offering a novel approach to simulating real-world faults in software systems. By bridging the gap between software fault injection techniques and real-world representativeness, PyResBugs provides researchers with a high-quality resource for advancing AI-driven automated testing in Python systems.