An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques

dc.authorscopusid 55625428500
dc.authorscopusid 55625218200
dc.authorscopusid 55931893500
dc.authorscopusid 55625653800
dc.authorscopusid 16642123000
dc.authorscopusid 16642447800
dc.contributor.author Koc,E.
dc.contributor.author Ersoy,N.
dc.contributor.author Andac,A.
dc.contributor.author Camlidere,Z.S.
dc.contributor.author Cereci,I.
dc.contributor.author Kilic,H.
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:43:50Z
dc.date.available 2024-07-05T15:43:50Z
dc.date.issued 2012
dc.department Atılım University en_US
dc.department-temp Koc E., Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey; Ersoy N., Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey; Andac A., Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey; Camlidere Z.S., Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey; Cereci I., Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey; Kilic H., Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey en_US
dc.description.abstract Automated maintenance of object-oriented software system designs via refactoring is a performance demanding combinatorial optimization problem. In this study, we made an empirical comparative study to see the performances of alternative search algorithms under a quality model defined by an aggregated software fitness metric. We handled 20 different refactoring actions that realize searches on design landscape defined by combination of 24 object-oriented software metrics. The investigated algorithms include random, steepest descent, multiple first descent, multiple steepest descent, simulated annealing and artificial bee colony searches. The study is realized by using a tool called A-CMA developed in Java that accepts bytecode compiled Java codes as its input. The empiricial study showed that multiple steepest descent and population-based artificial bee colony algorithms are two most suitable approaches for the efficient solution of the search based refactoring problem. © 2012 Springer-Verlag London Limited. en_US
dc.identifier.citationcount 19
dc.identifier.doi 10.1007/978-1-4471-2155-8-7
dc.identifier.endpage 66 en_US
dc.identifier.scopus 2-s2.0-84887839407
dc.identifier.startpage 59 en_US
dc.identifier.uri https://doi.org/10.1007/978-1-4471-2155-8-7
dc.identifier.uri https://hdl.handle.net/20.500.14411/3673
dc.institutionauthor Kılıç, Hürevren
dc.institutionauthor Cereci, İbrahim
dc.language.iso en en_US
dc.relation.ispartof Computer and Information Sciences II - 26th International Symposium on Computer and Information Sciences, ISCIS 2011 -- 26th Annual International Symposium on Computer and Information Science, ISCIS 2011 -- 26 September 2011 through 28 September 2011 -- London -- 100894 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 21
dc.subject Automated refactoring en_US
dc.subject Combinatorial optimization en_US
dc.subject Search-based software engineering en_US
dc.subject Software maintenance en_US
dc.subject Software metrics en_US
dc.title An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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