Techniques for evaluating fault prediction models
Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicabil...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Yue [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Empirical software engineering - Springer US, 1996, 13(2008), 5 vom: 12. Aug., Seite 561-595 |
|---|---|
| Übergeordnetes Werk: |
volume:13 ; year:2008 ; number:5 ; day:12 ; month:08 ; pages:561-595 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10664-008-9079-3 |
|---|
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OLC2071660064 |
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| 520 | |a Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. | ||
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10.1007/s10664-008-9079-3 doi (DE-627)OLC2071660064 (DE-He213)s10664-008-9079-3-p DE-627 ger DE-627 rakwb eng 004 VZ Jiang, Yue verfasserin aut Techniques for evaluating fault prediction models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. Fault-prediction models Model evaluation Predictive models in software engineering Empirical studies Cukic, Bojan aut Ma, Yan aut Enthalten in Empirical software engineering Springer US, 1996 13(2008), 5 vom: 12. Aug., Seite 561-595 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:13 year:2008 number:5 day:12 month:08 pages:561-595 https://doi.org/10.1007/s10664-008-9079-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 AR 13 2008 5 12 08 561-595 |
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10.1007/s10664-008-9079-3 doi (DE-627)OLC2071660064 (DE-He213)s10664-008-9079-3-p DE-627 ger DE-627 rakwb eng 004 VZ Jiang, Yue verfasserin aut Techniques for evaluating fault prediction models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. Fault-prediction models Model evaluation Predictive models in software engineering Empirical studies Cukic, Bojan aut Ma, Yan aut Enthalten in Empirical software engineering Springer US, 1996 13(2008), 5 vom: 12. Aug., Seite 561-595 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:13 year:2008 number:5 day:12 month:08 pages:561-595 https://doi.org/10.1007/s10664-008-9079-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 AR 13 2008 5 12 08 561-595 |
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10.1007/s10664-008-9079-3 doi (DE-627)OLC2071660064 (DE-He213)s10664-008-9079-3-p DE-627 ger DE-627 rakwb eng 004 VZ Jiang, Yue verfasserin aut Techniques for evaluating fault prediction models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. Fault-prediction models Model evaluation Predictive models in software engineering Empirical studies Cukic, Bojan aut Ma, Yan aut Enthalten in Empirical software engineering Springer US, 1996 13(2008), 5 vom: 12. Aug., Seite 561-595 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:13 year:2008 number:5 day:12 month:08 pages:561-595 https://doi.org/10.1007/s10664-008-9079-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 AR 13 2008 5 12 08 561-595 |
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10.1007/s10664-008-9079-3 doi (DE-627)OLC2071660064 (DE-He213)s10664-008-9079-3-p DE-627 ger DE-627 rakwb eng 004 VZ Jiang, Yue verfasserin aut Techniques for evaluating fault prediction models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. Fault-prediction models Model evaluation Predictive models in software engineering Empirical studies Cukic, Bojan aut Ma, Yan aut Enthalten in Empirical software engineering Springer US, 1996 13(2008), 5 vom: 12. Aug., Seite 561-595 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:13 year:2008 number:5 day:12 month:08 pages:561-595 https://doi.org/10.1007/s10664-008-9079-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 AR 13 2008 5 12 08 561-595 |
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Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. © Springer Science+Business Media, LLC 2008 |
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Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. © Springer Science+Business Media, LLC 2008 |
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Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment. © Springer Science+Business Media, LLC 2008 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2071660064</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503052507.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2008 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10664-008-9079-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2071660064</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10664-008-9079-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Yue</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Techniques for evaluating fault prediction models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2008</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Many statistical techniques have been proposed to predict fault-proneness of program modules in software engineering. Choosing the “best” candidate among many available models involves performance assessment and detailed comparison, but these comparisons are not simple due to the applicability of varying performance measures. Classifying a software module as fault-prone implies the application of some verification activities, thus adding to the development cost. Misclassifying a module as fault free carries the risk of system failure, also associated with cost implications. Methodologies for precise evaluation of fault prediction models should be at the core of empirical software engineering research, but have attracted sporadic attention. In this paper, we overview model evaluation techniques. In addition to many techniques that have been used in software engineering studies before, we introduce and discuss the merits of cost curves. Using the data from a public repository, our study demonstrates the strengths and weaknesses of performance evaluation techniques and points to a conclusion that the selection of the “best” model cannot be made without considering project cost characteristics, which are specific in each development environment.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fault-prediction models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Predictive models in software engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Empirical studies</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cukic, Bojan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Yan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Empirical software engineering</subfield><subfield code="d">Springer US, 1996</subfield><subfield code="g">13(2008), 5 vom: 12. 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