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On the application of genetic programming for software engineering predictive modeling: A systematic review
Ansvarig organisation
2011 (Engelska)Ingår i: Expert Systems with Applications, ISSN 0957-4174 , Vol. 38, nr 9, s. 11984-11997Artikel, forskningsöversikt (Refereegranskat) 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 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 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.

Ort, förlag, år, upplaga, sidor
Pergamon-Elsevier Science Ltd , 2011. Vol. 38, nr 9, s. 11984-11997
Nyckelord [en]
Systematic review, Genetic programmingm, Symbolic regression, Modeling
Nationell ämneskategori
Programvaruteknik
Identifikatorer
URN: urn:nbn:se:bth-7521DOI: 10.1016/j.eswa.2011.03.041ISI: 000291118500143Lokalt ID: oai:bth.se:forskinfo0DA8231B3E65A85CC12578BD003BAB5COAI: oai:DiVA.org:bth-7521DiVA, id: diva2:835145
Tillgänglig från: 2012-09-18 Skapad: 2011-06-28 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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