An empirical study of operating systems errors

Chou, Andy; Yang, Junfeng; Chelf, Benjamin; Hallem, Seth; Engler, Dawson

doi:10.1145/502034.502042

Cited by 482 publications

(126 citation statements)

References 13 publications

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“…They found that memory bugs and concurrency bugs accounted for a small portion of bug reports and semantic bugs were the major root causes. Chou et al [7] presented a study of operating system errors found in Linux and OpenBSD kernels. They found that device drivers had error rates up to three to seven times higher than the rest of the kernel, and that the bugs remained in the Linux kernel an average of 1.8 years before being fixed.…”

Section: Related Workmentioning

confidence: 99%

“…Over the years, many empirical studies have been performed to understand the characteristics of quality issues of conventional software systems (e.g., [24], [20], [7], [10], [2], [29]). However, to our best knowledge, few empirical study was carried out for large-scale Big Data computing platforms, which are a modern form of software system.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Study on Quality Issues of Production Big Data Platform

Zhou

Lou

Zhang

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Big Data computing platform has evolved to be a multi-tenant service. The service quality matters because system failure or performance slowdown could adversely affect business and user experience. To date, there is few study in literature on service quality issues of production Big Data computing platform. In this paper, we present an empirical study on the service quality issues of Microsoft ProductA, which is a company-wide multi-tenant Big Data computing platform, serving thousands of customers from hundreds of teams. ProductA has a well-defined escalation process (i.e., incident management process), which helps customers report service quality issues on 24/7 basis. This paper investigates the common symptom, causes and mitigation of service quality issues in Big Data platform. We conduct a comprehensive empirical study on 210 real service quality issues of ProductA. Our major findings include (1) 21.0% of escalations are caused by hardware faults; (2) 36.2% are caused by system side defects; (3) 37.2% are due to customer side faults. We also studied the general diagnosis process and the commonly adopted mitigation solutions. Our study results provide valuable guidance on improving existing development and maintenance practice of production Big Data platform, and motivate tool support.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Empirical Study on Quality Issues of Production Big Data Platform

Zhou

Lou

Zhang

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

View full text Add to dashboard Cite

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“…Palix et al [19] try to reproduce a ten year old analysis on Linux by Chou et al [8] in order to investigate the evolutionary development of Linux across the last decade. As the old experiment misses to state the exact configuration that was used, the environment could only be approximated.…”

Section: Related Workmentioning

confidence: 99%

A robust approach for variability extraction from the Linux build system

Dietrich

Tartler

Schröder-Preikschat

et al. 2012

Proceedings of the 16th International Software Product Line Conference - Volume 1

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With more than 11,000 optional and alternative features, the Linux kernel is a highly configurable piece of software. Linux is generally perceived as a textbook example for preprocessor-based product derivation, but more than 65 percent of all features are actually handled by the build system. Hence, variability-aware static analysis tools have to take the build system into account.However, extracting variability information from the build system is difficult due to the declarative and turing-complete MAKE language. Existing approaches based on text processing do not cover this challenges and tend to be tailored to a specific Linux version and architecture. This renders them practically unusable as a basis for variability-aware tool support -Linux is a moving target! We describe a robust approach for extracting implementation variability from the Linux build system. Instead of extracting the variability information by a text-based analysis of all build scripts, our approach exploits the build system itself to produce this information. As our results show, our approach is robust and works for all versions and architectures from the (git-)history of Linux.

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“…At the same time every bug in the Linux kernel is critical [2]. Researches revealed that most of bugs in the Linux kernel are located in its modules (modules contain approximately 7 times more bugs than the kernel itself) [3]. One of the approaches to find those bugs is using static verifiers.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Number of Static Verifier Traces to Reduce Time for Finding Bugs in Linux Kernel Modules

Mordan¹,

Novikov²

2014

Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering

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Abstract-Static verifiers usually stop after they find a first bug in a program under analysis. This slows down the process of finding and fixing of bugs of the same kind in a given Linux kernel module. In order to solve this problem we used the static verifier CPAchecker with option to continue analysis after finding of a first bug. Besides we extended LDV Tools -a toolset for verification of Linux kernel modules -for finding several bugs in a given module against a specified rule specification. But first experiments revealed a new problem -the verifier produced too many similar traces. The given paper introduces a formal definition of equivalent traces and presents different comparison algorithms and a semi-automated approach, which makes possible to find several bugs in a given Linux kernel module against a specified rule specification at once.

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An empirical study of operating systems errors

Cited by 482 publications

References 13 publications

An Empirical Study on Quality Issues of Production Big Data Platform

An Empirical Study on Quality Issues of Production Big Data Platform

A robust approach for variability extraction from the Linux build system

Minimizing the Number of Static Verifier Traces to Reduce Time for Finding Bugs in Linux Kernel Modules

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