Kılıç, HürevrenKoc, EkinErsoy, NurCereci, İbrahimAndac, AliCamlidere, Zelal SedaCereci, IbrahimKilic, HurevrenComputer Engineering2024-07-052024-07-05201215978144712154110.1007/978-1-4471-2155-8_7https://doi.org/10.1007/978-1-4471-2155-8_7https://hdl.handle.net/20.500.14411/1301Kilic, Hurevren/0000-0002-9058-0365; KILIC, HUREVREN/0000-0003-2647-8451Automated 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.eninfo:eu-repo/semantics/closedAccessSearch-based software engineeringCombinatorial optimizationAutomated refactoringSoftware maintenanceSoftware metricsConference Object59+WOS:000398249500007