How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2019.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 1; 2025: 2; 2026: 2.
11 papers.
- 2021A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysiscited by 6
- 2026TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generationcited by 5
- 2025AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Modelcited by 4
- 2023A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detectioncited by 3
- 2024LUK: Empowering Log Understanding with Expert Knowledge from Large Language Modelscited by 2
- 2021Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learningcited by 1
- 2025PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injectioncited by 1
- 2021Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessmentcited by 0
- 2022syslrn: Learning What to Monitor for Efficient Anomaly Detectioncited by 0
- 2022Failure Identification from Unstable Log Data using Deep Learningcited by 0
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requestscited by 0
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests23 Jul 2026
- 2026TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation11 Feb 2026
- 2025PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection9 May 2025
- 2025AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model19 Jan 2025
- 2024LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models3 Sep 2024
- 2023A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection31 Jul 2023
- 2022Failure Identification from Unstable Log Data using Deep Learning6 Apr 2022
- 2022syslrn: Learning What to Monitor for Efficient Anomaly Detection29 Mar 2022
- 2021A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis24 Oct 2021
- 2021Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment6 Oct 2021
- 2021Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning29 Jun 2021