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DConfusion: A technique to allow cross study performance evaluation of fault prediction studies

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DConfusion : A technique to allow cross study performance evaluation of fault prediction studies. / Bowes, D.; Hall, T.; Gray, D.

In: Automated Software Engineering, Vol. 21, No. 2, 04.2014, p. 287-313.

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Bowes, D. ; Hall, T. ; Gray, D. / DConfusion : A technique to allow cross study performance evaluation of fault prediction studies. In: Automated Software Engineering. 2014 ; Vol. 21, No. 2. pp. 287-313.

Bibtex

@article{000231adee0d4677a077327dda007f74,
title = "DConfusion: A technique to allow cross study performance evaluation of fault prediction studies",
abstract = "There are many hundreds of fault prediction models published in the literature. The predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly comparable. This lack of comparability means that it is often difficult to evaluate the performance of one model against another. Our aim is to present an approach that allows other researchers and practitioners to transform many performance measures back into a confusion matrix. Once performance is expressed in a confusion matrix alternative preferred performance measures can then be derived. Our approach has enabled us to compare the performance of 600 models published in 42 studies. We demonstrate the application of our approach on 8 case studies, and discuss the advantages and implications of doing this.",
keywords = "fault, confusion matrix, machine learning",
author = "D. Bowes and T. Hall and D. Gray",
year = "2014",
month = apr,
doi = "10.1007/s10515-013-0129-8",
language = "English",
volume = "21",
pages = "287--313",
journal = "Automated Software Engineering",
issn = "0928-8910",
publisher = "Springer Netherlands",
number = "2",

}

RIS

TY - JOUR

T1 - DConfusion

T2 - A technique to allow cross study performance evaluation of fault prediction studies

AU - Bowes, D.

AU - Hall, T.

AU - Gray, D.

PY - 2014/4

Y1 - 2014/4

N2 - There are many hundreds of fault prediction models published in the literature. The predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly comparable. This lack of comparability means that it is often difficult to evaluate the performance of one model against another. Our aim is to present an approach that allows other researchers and practitioners to transform many performance measures back into a confusion matrix. Once performance is expressed in a confusion matrix alternative preferred performance measures can then be derived. Our approach has enabled us to compare the performance of 600 models published in 42 studies. We demonstrate the application of our approach on 8 case studies, and discuss the advantages and implications of doing this.

AB - There are many hundreds of fault prediction models published in the literature. The predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly comparable. This lack of comparability means that it is often difficult to evaluate the performance of one model against another. Our aim is to present an approach that allows other researchers and practitioners to transform many performance measures back into a confusion matrix. Once performance is expressed in a confusion matrix alternative preferred performance measures can then be derived. Our approach has enabled us to compare the performance of 600 models published in 42 studies. We demonstrate the application of our approach on 8 case studies, and discuss the advantages and implications of doing this.

KW - fault

KW - confusion matrix

KW - machine learning

U2 - 10.1007/s10515-013-0129-8

DO - 10.1007/s10515-013-0129-8

M3 - Journal article

VL - 21

SP - 287

EP - 313

JO - Automated Software Engineering

JF - Automated Software Engineering

SN - 0928-8910

IS - 2

ER -