Build system analysis with link prediction

Xin Xia; David Lo; Xinyu Wang; Bo Zhou

doi:10.1145/2554850.2555134

Build system analysis with link prediction

Xin Xia, David Lo, Xinyu Wang, Bo Zhou

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

10 Citations (Scopus)

Abstract

Compilation is an important step in building working software system. To compile large systems, typically build systems, such as make, are used. In this paper, we investigate a new research problem for build configuration file (e.g., Makefile) analysis: how to predict missed dependencies in a build configuration file. We refer to this problem as dependency mining. Based on a Makefile, we build a dependency graph capturing various relationships defined in the Makefile. By representing a Makefile as a dependency graph, we map the dependency mining problem to a link prediction problem, and leverage 9 state-of-the-art link prediction algorithms to solve it. We collected Makefiles from 7 open source projects to evaluate the effectiveness of the algorithms.

Original language	English
Title of host publication	Proceedings - The 29th Annual ACM Symposium on Applied Computing, SAC 2014
Subtitle of host publication	March 24 – 28, 2014, Gyeongju, Korea
Editors	Chih-Cheng Hung , Jiman Hong
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	1184-1186
Number of pages	3
ISBN (Print)	9781450324694
DOIs	https://doi.org/10.1145/2554850.2555134
Publication status	Published - 2014
Externally published	Yes
Event	ACM Symposium on Applied Computing 2014 - Gyeongju, Korea, South Duration: 24 Mar 2014 → 28 Mar 2014 Conference number: 29th http://www.sigapp.org/sac/sac2014/ https://dl.acm.org/doi/proceedings/10.1145/2554850 (Proceedings)

Conference

Conference	ACM Symposium on Applied Computing 2014
Abbreviated title	SAC 2014
Country/Territory	Korea, South
City	Gyeongju
Period	24/03/14 → 28/03/14
Internet address	http://www.sigapp.org/sac/sac2014/ https://dl.acm.org/doi/proceedings/10.1145/2554850 (Proceedings)

Keywords

Build System
Link Prediction
Makefile

Access to Document

10.1145/2554850.2555134

Cite this

@inproceedings{b1897fd2951d42088084265335d7e0b6,

title = "Build system analysis with link prediction",

abstract = "Compilation is an important step in building working software system. To compile large systems, typically build systems, such as make, are used. In this paper, we investigate a new research problem for build configuration file (e.g., Makefile) analysis: how to predict missed dependencies in a build configuration file. We refer to this problem as dependency mining. Based on a Makefile, we build a dependency graph capturing various relationships defined in the Makefile. By representing a Makefile as a dependency graph, we map the dependency mining problem to a link prediction problem, and leverage 9 state-of-the-art link prediction algorithms to solve it. We collected Makefiles from 7 open source projects to evaluate the effectiveness of the algorithms.",

keywords = "Build System, Link Prediction, Makefile",

author = "Xin Xia and David Lo and Xinyu Wang and Bo Zhou",

year = "2014",

doi = "10.1145/2554850.2555134",

language = "English",

isbn = "9781450324694",

pages = "1184--1186",

editor = "{Hung }, {Chih-Cheng } and Hong, {Jiman }",

booktitle = "Proceedings - The 29th Annual ACM Symposium on Applied Computing, SAC 2014",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "ACM Symposium on Applied Computing 2014, SAC 2014 ; Conference date: 24-03-2014 Through 28-03-2014",

url = "http://www.sigapp.org/sac/sac2014/, https://dl.acm.org/doi/proceedings/10.1145/2554850",

}

Xia, X, Lo, D, Wang, X & Zhou, B 2014, Build system analysis with link prediction. in C-C Hung & J Hong (eds), Proceedings - The 29th Annual ACM Symposium on Applied Computing, SAC 2014: March 24 – 28, 2014, Gyeongju, Korea . Association for Computing Machinery (ACM), New York NY USA, pp. 1184-1186, ACM Symposium on Applied Computing 2014, Gyeongju, Korea, South, 24/03/14. https://doi.org/10.1145/2554850.2555134

Build system analysis with link prediction. / Xia, Xin; Lo, David; Wang, Xinyu et al.
Proceedings - The 29th Annual ACM Symposium on Applied Computing, SAC 2014: March 24 – 28, 2014, Gyeongju, Korea . ed. / Chih-Cheng Hung ; Jiman Hong. New York NY USA: Association for Computing Machinery (ACM), 2014. p. 1184-1186.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Build system analysis with link prediction

AU - Xia, Xin

AU - Lo, David

AU - Wang, Xinyu

AU - Zhou, Bo

N1 - Conference code: 29th

PY - 2014

Y1 - 2014

N2 - Compilation is an important step in building working software system. To compile large systems, typically build systems, such as make, are used. In this paper, we investigate a new research problem for build configuration file (e.g., Makefile) analysis: how to predict missed dependencies in a build configuration file. We refer to this problem as dependency mining. Based on a Makefile, we build a dependency graph capturing various relationships defined in the Makefile. By representing a Makefile as a dependency graph, we map the dependency mining problem to a link prediction problem, and leverage 9 state-of-the-art link prediction algorithms to solve it. We collected Makefiles from 7 open source projects to evaluate the effectiveness of the algorithms.

AB - Compilation is an important step in building working software system. To compile large systems, typically build systems, such as make, are used. In this paper, we investigate a new research problem for build configuration file (e.g., Makefile) analysis: how to predict missed dependencies in a build configuration file. We refer to this problem as dependency mining. Based on a Makefile, we build a dependency graph capturing various relationships defined in the Makefile. By representing a Makefile as a dependency graph, we map the dependency mining problem to a link prediction problem, and leverage 9 state-of-the-art link prediction algorithms to solve it. We collected Makefiles from 7 open source projects to evaluate the effectiveness of the algorithms.

KW - Build System

KW - Link Prediction

KW - Makefile

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DO - 10.1145/2554850.2555134

M3 - Conference Paper

AN - SCOPUS:84905650920

SN - 9781450324694

SP - 1184

EP - 1186

BT - Proceedings - The 29th Annual ACM Symposium on Applied Computing, SAC 2014

A2 - Hung , Chih-Cheng

A2 - Hong, Jiman

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

T2 - ACM Symposium on Applied Computing 2014

Y2 - 24 March 2014 through 28 March 2014

ER -

Build system analysis with link prediction

Abstract

Conference

Keywords

Access to Document

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