Koc,E.Ersoy,N.Andac,A.Camlidere,Z.S.Cereci,I.Kilic,H.Computer Engineering2024-07-052024-07-0520121910.1007/978-1-4471-2155-8-72-s2.0-84887839407https://doi.org/10.1007/978-1-4471-2155-8-7https://hdl.handle.net/20.500.14411/3673Automated 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.eninfo:eu-repo/semantics/closedAccessAutomated refactoringCombinatorial optimizationSearch-based software engineeringSoftware maintenanceSoftware metricsConference Object5966