An Empirical Study of Fault Triggers in the Linux Operating System: An Evolutionary Perspective | Zendy

Guanping Xiao | Zendy; Zheng Zheng | Zendy; Beibei Yin | Zendy; Kishor S. Trivedi | Zendy; Xiaoting Du | Zendy; KaiYuan Cai | Zendy

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An Empirical Study of Fault Triggers in the Linux Operating System: An Evolutionary Perspective

Author(s) -

Guanping Xiao,

Zheng Zheng,

Beibei Yin,

Kishor S. Trivedi,

Xiaoting Du,

KaiYuan Cai

Publication year - 2019

Publication title -

ieee transactions on reliability

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.032

H-Index - 102

eISSN - 1558-1721

pISSN - 0018-9529

DOI - 10.1109/tr.2019.2916204

Subject(s) - linux kernel , computer science , betweenness centrality , software bug , software maintenance , empirical research , closeness , regression testing , perspective (graphical) , software regression , software evolution , maintenance engineering , data mining , software , operating system , software quality , reliability engineering , software system , artificial intelligence , software development , engineering , statistics , mathematics , mathematical analysis , software construction , centrality

This paper presents an empirical study of 5741 bug reports for the Linux kernel from an evolutionary perspective, with the aim of obtaining a deep understanding of bug characteristics in the Linux operating system. Bug classification is performed based on the fault triggering conditions, followed by an analysis of the proportions and evolution of the bug types as well as comparisons among versions, products, and repair locations. In addition, an analysis of regression bugs and the relationship between the types of bugs and the time needed to fix them are presented. Moreover, a procedure for the analysis of bug type characteristics based on complex network metrics is proposed, and four network metrics, i.e., degree, clustering coefficient, betweenness, and closeness, are utilized to further investigate the relationship between bug types and software metrics. In this paper, 22 interesting findings based on the empirical results are revealed, and guidance based on these findings is provided for developers and users.

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