Mutation-aware fault prediction

David Bowes; Tracy Hall; Mark Harman; Yue Jia; Federica Sarro; Fan Wu

doi:10.1145/2931037.2931039

Mutation-aware fault prediction

David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, Fan Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Citations (Scopus)

Abstract

We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.

Original language	English
Title of host publication	ISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis
Editors	Andreas Zeller, Abhik Roychoudhury
Place of Publication	Saarbrucken
Publisher	ACM Press
Pages	330-341
Number of pages	12
ISBN (Electronic)	978-145034390-9
DOIs	https://doi.org/10.1145/2931037.2931039
Publication status	Published - 18 Jul 2016
Event	ISSTA 2016: 25th International Symposium on Software Testing and Analysis - Saarbrucken, Germany Duration: 18 Jul 2016 → 20 Jul 2016

Conference

Conference	ISSTA 2016
Country/Territory	Germany
Period	18/07/16 → 20/07/16

Keywords

Empirical study
Mutation testing
Software defect prediction
Software fault prediction
Software metrics

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10.1145/2931037.2931039

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@inproceedings{024440f54b8848388ad824e7a4817574,

title = "Mutation-aware fault prediction",

abstract = "We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.",

keywords = "Empirical study, Mutation testing, Software defect prediction, Software fault prediction, Software metrics",

author = "David Bowes and Tracy Hall and Mark Harman and Yue Jia and Federica Sarro and Fan Wu",

note = "David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, and Fan Wu, 'Mutation-aware fault prediction', in Proceedings of the 25th International Symposium on Software Testing and Analysis, ISSTA 2016. Saarbrucken, Germany, 18-20 July September 2016. Andreas Zeller and Abhik Roychoudhury eds., e-ISBN 978-145034390-9, doi: 10.1145/2931037.2931039. The ACM Digital Library is published by the Association for Computing Machinery. Copyright {\textcopyright} 2017 ACM, Inc.; ISSTA 2016 : 25th International Symposium on Software Testing and Analysis ; Conference date: 18-07-2016 Through 20-07-2016",

year = "2016",

month = jul,

day = "18",

doi = "10.1145/2931037.2931039",

language = "English",

pages = "330--341",

editor = "Andreas Zeller and Roychoudhury, {Abhik }",

booktitle = "ISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis",

publisher = "ACM Press",

address = "United States",

}

TY - GEN

T1 - Mutation-aware fault prediction

AU - Bowes, David

AU - Hall, Tracy

AU - Harman, Mark

AU - Jia, Yue

AU - Sarro, Federica

AU - Wu, Fan

N1 - David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, and Fan Wu, 'Mutation-aware fault prediction', in Proceedings of the 25th International Symposium on Software Testing and Analysis, ISSTA 2016. Saarbrucken, Germany, 18-20 July September 2016. Andreas Zeller and Abhik Roychoudhury eds., e-ISBN 978-145034390-9, doi: 10.1145/2931037.2931039. The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2017 ACM, Inc.

PY - 2016/7/18

Y1 - 2016/7/18

N2 - We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.

AB - We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.

KW - Empirical study

KW - Mutation testing

KW - Software defect prediction

KW - Software fault prediction

KW - Software metrics

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U2 - 10.1145/2931037.2931039

DO - 10.1145/2931037.2931039

M3 - Conference contribution

AN - SCOPUS:84984918495

SP - 330

EP - 341

BT - ISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis

A2 - Zeller, Andreas

A2 - Roychoudhury, Abhik

PB - ACM Press

CY - Saarbrucken

T2 - ISSTA 2016

Y2 - 18 July 2016 through 20 July 2016

ER -

Mutation-aware fault prediction

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