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Autores principales: Liu, Yuhao, Zhou, Yingnan, Zhang, Hanfeng, Chang, Zhiwei, Xu, Sihan, Jia, Yan, Wang, Wei, Hu, Juncheng, Liu, Zheli
Formato: Preprint
Publicado: 2024
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Acceso en línea:https://arxiv.org/abs/2412.11121
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author Liu, Yuhao
Zhou, Yingnan
Zhang, Hanfeng
Chang, Zhiwei
Xu, Sihan
Jia, Yan
Wang, Wei
Hu, Juncheng
Liu, Zheli
author_facet Liu, Yuhao
Zhou, Yingnan
Zhang, Hanfeng
Chang, Zhiwei
Xu, Sihan
Jia, Yan
Wang, Wei
Hu, Juncheng
Liu, Zheli
contents Software misconfiguration has consistently been a major reason for software failures. Over the past two decades, much work has been done to detect and diagnose software misconfigurations. However, there is still a gap between real-world misconfigurations and the literature. It is desirable to investigate whether existing taxonomy and tools are applicable for real-world misconfigurations in modern software. In this paper, we conduct an empirical study on 772 real-world misconfiguration issues, based on which we propose a novel classification of the root causes of software misconfigurations, i.e., constraint violation, resource unavailability, component integration error, and configuration semantic misinterpretation. Then, we systematically review the literature on misconfiguration troubleshooting to study the trends of research and the practicality of the tools and datasets in this field. We find that the research targets have changed from system and infrastructure software to advanced applications (e.g., cloud service). Meanwhile, research on non-crash misconfigurations has also grown significantly. Despite the progress, a majority of studies lack reproducibility due to the unavailable tools and evaluation datasets. In total, only eleven tools and four datasets are publicly available. We analyze the trends of existing literature on misconfiguration troubleshooting, summarize the challenges that users are faced with, and highlight the suggestions to mitigate and diagnose software misconfigurations. We release the real-world dataset of misconfiguration issues for follow-up research.
format Preprint
id arxiv_https___arxiv_org_abs_2412_11121
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Rethinking Software Misconfigurations in the Real World: An Empirical Study and Literature Analysis
Liu, Yuhao
Zhou, Yingnan
Zhang, Hanfeng
Chang, Zhiwei
Xu, Sihan
Jia, Yan
Wang, Wei
Hu, Juncheng
Liu, Zheli
Software Engineering
D.2
Software misconfiguration has consistently been a major reason for software failures. Over the past two decades, much work has been done to detect and diagnose software misconfigurations. However, there is still a gap between real-world misconfigurations and the literature. It is desirable to investigate whether existing taxonomy and tools are applicable for real-world misconfigurations in modern software. In this paper, we conduct an empirical study on 772 real-world misconfiguration issues, based on which we propose a novel classification of the root causes of software misconfigurations, i.e., constraint violation, resource unavailability, component integration error, and configuration semantic misinterpretation. Then, we systematically review the literature on misconfiguration troubleshooting to study the trends of research and the practicality of the tools and datasets in this field. We find that the research targets have changed from system and infrastructure software to advanced applications (e.g., cloud service). Meanwhile, research on non-crash misconfigurations has also grown significantly. Despite the progress, a majority of studies lack reproducibility due to the unavailable tools and evaluation datasets. In total, only eleven tools and four datasets are publicly available. We analyze the trends of existing literature on misconfiguration troubleshooting, summarize the challenges that users are faced with, and highlight the suggestions to mitigate and diagnose software misconfigurations. We release the real-world dataset of misconfiguration issues for follow-up research.
title Rethinking Software Misconfigurations in the Real World: An Empirical Study and Literature Analysis
topic Software Engineering
D.2
url https://arxiv.org/abs/2412.11121