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On the application of genetic programming for software engineering predictive modeling: A systematic review
Blekinge Inst Technol. (IS (Embedded Systems))ORCID-id: 0000-0003-0611-2655
Blekinge Inst Technol.
2011 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, nr 9, s. 11984-11997Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons 20 with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different 22 independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995–2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modeling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling; the results are inconclusive for software cost/effort/size estimation.

sted, utgiver, år, opplag, sider
2011. Vol. 38, nr 9, s. 11984-11997
HSV kategori
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URN: urn:nbn:se:mdh:diva-22296DOI: 10.1016/j.eswa.2011.03.041ISI: 000291118500143OAI: oai:DiVA.org:mdh-22296DiVA, id: diva2:661103
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Project_ExternalTilgjengelig fra: 2013-10-31 Laget: 2013-10-31 Sist oppdatert: 2017-12-06bibliografisk kontrollert

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