On the application of genetic programming for software engineering predictive modeling: A systematic review

Document type: Journal Articles
Article type: Review
Peer reviewed: Yes
Author(s): Wasif Afzal, Richard Torkar
Title: On the application of genetic programming for software engineering predictive modeling: A systematic review
Journal: Expert Systems with Applications
Year: 2011
Volume: 38
Issue: 9
Pagination: 11984-11997
ISSN: 0957-4174
Publisher: Pergamon-Elsevier Science Ltd
URI/DOI: 10.1016/j.eswa.2011.03.041
ISI number: 000291118500143
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com
Language: English
Abstract: 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.
Subject: Software Engineering\General
Keywords: Systematic review, Genetic programmingm, Symbolic regression, Modeling
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