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Search-based predic...
Search-based prediction of fault count data
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- Afzal, Wasif (author)
- Blekinge Institute of Technology,IS (Embedded Systems)
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- Torkar, Richard (author)
- Blekinge Institute of Technology
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- Feldt, Robert (author)
- Blekinge Institute of Technology
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(creator_code:org_t)
- ISBN 9780769536750
- Windsor : IEEE Computer Society, 2009
- 2009
- English.
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In: Proceedings - 1st International Symposium on Search Based Software Engineering, SSBSE 2009. - Windsor : IEEE Computer Society. - 9780769536750 ; , s. 35-38
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Abstract
Subject headings
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- Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-defined function, symbolic regression finds a general function, with coefficients, fitting the given set of data points. The concepts of symbolic regression using genetic programming can be used to evolve a model for fault count predictions. Such a model has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictions using genetic programming and comparing the results with traditional approaches to compare efficiency gains.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- search-based
- fault prediciton
Publication and Content Type
- ref (subject category)
- kon (subject category)
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