Standard

Fever: Extracting feature-oriented changes from commits. / Dintzner, Nicolas; Van Deursen, Arie; Pinzger, Martin.

Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016. Association for Computing Machinery (ACM), 2016. p. 85-96.

Research output: Scientific - peer-reviewConference contribution

Harvard

Dintzner, N, Van Deursen, A & Pinzger, M 2016, Fever: Extracting feature-oriented changes from commits. in Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016. Association for Computing Machinery (ACM), pp. 85-96, 13th Working Conference on Mining Software Repositories, MSR 2016, Austin, United States, 14-15 May. DOI: 10.1145/2901739.2901755

APA

Dintzner, N., Van Deursen, A., & Pinzger, M. (2016). Fever: Extracting feature-oriented changes from commits. In Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016. (pp. 85-96). Association for Computing Machinery (ACM). DOI: 10.1145/2901739.2901755

Vancouver

Dintzner N, Van Deursen A, Pinzger M. Fever: Extracting feature-oriented changes from commits. In Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016. Association for Computing Machinery (ACM). 2016. p. 85-96. Available from, DOI: 10.1145/2901739.2901755

Author

Dintzner, Nicolas; Van Deursen, Arie; Pinzger, Martin / Fever: Extracting feature-oriented changes from commits.

Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016. Association for Computing Machinery (ACM), 2016. p. 85-96.

Research output: Scientific - peer-reviewConference contribution

BibTeX

@inbook{292c2743a21949a2bc2e9a2add8992f7,
title = "Fever: Extracting feature-oriented changes from commits",
keywords = "Co-evolution, Feature, Highly variable systems, Variability",
author = "Nicolas Dintzner and {Van Deursen}, Arie and Martin Pinzger",
year = "2016",
month = "5",
doi = "10.1145/2901739.2901755",
pages = "85--96",
booktitle = "Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - CHAP

T1 - Fever: Extracting feature-oriented changes from commits

AU - Dintzner,Nicolas

AU - Van Deursen,Arie

AU - Pinzger,Martin

PY - 2016/5/14

Y1 - 2016/5/14

N2 - The study of the evolution of highly configurable systems requires a thorough understanding of thee core ingredients of such systems: (1) the underlying variability model; (2) the assets that together implement the configurable features; and (3) the mapping from variable features to actual assets. Unfortunately, to date no systematic way to obtain such information at a sufficiently fine grained level exists. To remedy this problem we propose FEVER and its instantiation for the Linux kernel. FEVER extracts detailed information on changes in variability models (KConfig files), assets (preprocessor based C code), and mappings (Makefiles). We describe how FEVER works, and apply it to several releases of the Linux kernel. Our evaluation on 300 randomly selected commits, from two different releases, shows our results are accurate in 82.6% of the commits. Furthermore, we illustrate how the populated FEVER graph database thus obtained can be used in typical Linux engineering tasks.

AB - The study of the evolution of highly configurable systems requires a thorough understanding of thee core ingredients of such systems: (1) the underlying variability model; (2) the assets that together implement the configurable features; and (3) the mapping from variable features to actual assets. Unfortunately, to date no systematic way to obtain such information at a sufficiently fine grained level exists. To remedy this problem we propose FEVER and its instantiation for the Linux kernel. FEVER extracts detailed information on changes in variability models (KConfig files), assets (preprocessor based C code), and mappings (Makefiles). We describe how FEVER works, and apply it to several releases of the Linux kernel. Our evaluation on 300 randomly selected commits, from two different releases, shows our results are accurate in 82.6% of the commits. Furthermore, we illustrate how the populated FEVER graph database thus obtained can be used in typical Linux engineering tasks.

KW - Co-evolution

KW - Feature

KW - Highly variable systems

KW - Variability

UR - http://www.scopus.com/inward/record.url?scp=84974601953&partnerID=8YFLogxK

U2 - 10.1145/2901739.2901755

DO - 10.1145/2901739.2901755

M3 - Conference contribution

SP - 85

EP - 96

BT - Proceedings - 13th Working Conference on Mining Software Repositories, MSR 2016

PB - Association for Computing Machinery (ACM)

ER -

ID: 8203724