# How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform

arXiv: https://arxiv.org/abs/1907.04055 · HTML: https://mlsanity.com/paper/1907.04055/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 11 of 11)

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- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning](https://mlsanity.com/paper/2106.15182/) (2021)
- [PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection](https://mlsanity.com/paper/2505.05777/) (2025)
- [Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment](https://mlsanity.com/paper/2110.03431/) (2021)
- [syslrn: Learning What to Monitor for Efficient Anomaly Detection](https://mlsanity.com/paper/2203.15324/) (2022)
- [Failure Identification from Unstable Log Data using Deep Learning](https://mlsanity.com/paper/2204.02636/) (2022)
- [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026)

## Abstract (arXiv)

Cloud management systems provide abstractions and APIs for programmatically configuring cloud infrastructures. Unfortunately, residual software bugs in these systems can potentially lead to high-severity failures, such as prolonged outages and data losses. In this paper, we investigate the impact of failures in the context widespread OpenStack cloud management system, by performing fault injection and by analyzing the impact of the resulting failures in terms of fail-stop behavior, failure detection through logging, and failure propagation across components. The analysis points out that most of the failures are not timely detected and notified; moreover, many of these failures can silently propagate over time and through components of the cloud management system, which call for more thorough run-time checks and fault containment.