Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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| Original language | English |
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| Title of host publication | Procs of the 8th Int Conf on Predictive Models in Software Engineering |
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| Subtitle of host publication | PROMISE'12 |
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| Place of Publication | New York, NY, USA |
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| Publisher | ACM Press |
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| Pages | 109-118 |
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| Number of pages | 10 |
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| ISBN (Print) | 978-1-4503-1241-7 |
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| DOIs | |
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| Publication status | Published - 2012 |
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| Name | PROMISE '12 |
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| Publisher | ACM |
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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 of categorical studies 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 several case studies, and discuss
the advantages and implications of doing this.
Notes
Awarded Best Paper in Conference.
"© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Procs of the 8th Int Conf on Predictive Models in Software Engineering http://doi.acm.org/10.1145/2365324.2365338"
ID: 1189706