Kılıç, Hürevren

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H., Kilic
Hürevren, Kılıç
Hurevren, Kilic
Kiliç H.
H.,Kilic
H.,Kılıç
H., Kılıç
K., Hürevren
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Kılıç,H.
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Hürevren Kılıç
K.,Hürevren
K., Hurevren
Kilic H.
Kılıç H.
Kılıç, Hürevren
Kilic, Hurevren
Kilic,Hurevren
Job Title
Profesör Doktor
Email Address
hurevren.kilic@atilim.edu.tr
Main Affiliation
Computer Engineering
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Website
https://www.atilim.edu.tr/tr/bilgisayar-muhendisligi-ana-bilim-dali/page/1620/akademik-kadro
ORCID ID
0000-0002-9058-0365
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Scholarly Output

27

Articles

6

Citation Count

58

Supervised Theses

5

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2008 - 200912010 - 20123
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Author: Cereci, Ibrahim

Scholarly Output Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Conference Object
    Citation - WoS: 13
    Search-Based Parallel Refactoring Using Population-Based Direct Approaches
    (Springer-verlag Berlin, 2011) Kilic, Hurevren; Koc, Ekin; Cereci, Ibrahim; Computer Engineering
    Automated software refactoring is known to be one of the "hard" combinatorial optimization problems of the search-based software engineering field. The difficulty is mainly due to candidate solution representation, objective function description and necessity of functional behavior preservation of software. The problem is formulated as a combinatorial optimization problem whose objective function is characterized by an aggregate of object-oriented metrics or pareto-front solution description. In our recent empirical study, we have reported the results of a comparison among alternative search algorithms applied for the same problem: pure random, steepest descent, multiple first descent, simulated annealing, multiple steepest descent and artificial bee colony searches. The main goal of the study was to investigate potential of alternative multiple and population-based search techniques. The results showed that multiple steepest descent and artificial bee colony algorithms were most suitable two approaches for an efficient solution of the problem. An important observation was either with depth-oriented multiple steepest descent or breadth-oriented population-based artficial bee colony searches, better results could be obtained through higher number of executions supported by a lightweight solution representation. On the other hand different from multiple steepest descent search, population-based, scalable and being suitable for parallel execution characteristics of artificial bee colony search made the population-based choices to be the topic of this empirical study. I In this study, we report the search-based parallel refactoring results of an empirical comparative study among three population-based search techniques namely, artificial bee colony search, local beam search and stochastic beam search and a non-populated technique multiple steepest descent as the baseline. For our purpose, we used parallel features of our prototype automated refactoring tool A-CMA written in Java language. A-CMA accepts bytecode compiled Java codes as its input. It supports 20 different refactoring actions that realize searches on design landscape defined by an adhoc quality model being an aggregation of 24 object-oriented software metrics. We experimented 6 input programs written in Java where 5 of them being open source codes and one student project code. The empirical results showed that for almost all of the considered input programs with different run parameter settings, local beam search is the most suitable population-based search technique for the efficient solution of the search-based parallel refactoring problem in terms of mean and maximum normalized quality gain. However, we observed that the computational time requirement for local beam search becomes rather high when the beam size exceeds 60. On the other hand, even though it is not able to identify high quality designs for less populated search setups, time-efficiency and scalability properties of artificial bee colony search makes it a good choice for population sizes >= 200.
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    Citation - WoS: 0
    Citation - Scopus: 0
    F-Actor: a Multiagent Gaming Environment for Controlling Virtual Flow Networks
    (Univ Wolverhampton, 2008) Ocal, Ilter Kagan; Cevik, Ahmet; Cereci, Ibrahim; Kilic, Hurevren; Computer Engineering; Computer Engineering
    A gaming environment that enables agent-based local control of a configurable virtual flow network is developed. The gaming software what we call F-Actor provides a graph-based discrete virtual control environment on which user-developed controller agents reside and act according to their assigned design goals. Runtime performances of user-developed controller agent codes are made observable through a graphical user interface. The proposed game can be played by different developers having different level of control and programming knowledge. By playing with F-Actor, engineers (or students) can make practices on a virtual flow environment and try alternative intelligent control algorithms before their potential implementations on field.
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    Conference Object
    Citation - WoS: 2
    CAWP A Combinatorial Auction Web Platform
    (Scitepress, 2010) Cereci, Ibrahim; Kilic, Hurevren; Computer Engineering
    Online auctions, including online Combinatorial Auctions, are important examples of e-commerce applications. In this paper, a Combinatorial Auction Web Platform (CAWP) is introduced. The platform enables both product selling and buying capabilities that can be realized in a combinatorial way. CAWP supports a Sealed-Bid Single-Unit type of Combinatorial Auctions. Easy customization for any selected problem domain is a distinguished feature of CAWP. Platform users are not expected to have any technical knowledge about how to solve the Winner Determination Problem (WDP) known to be critical for profit maximization of the auctioneers in Combinatorial Auctions.
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    Citation - WoS: 16
    An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques
    (Springer-verlag London Ltd, 2012) Koc, Ekin; Ersoy, Nur; Andac, Ali; Camlidere, Zelal Seda; Cereci, Ibrahim; Kilic, Hurevren; Computer Engineering
    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.