Kılıç, Hürevren

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Profile URL
https://hdl.handle.net/20.500.14411/6745
Name Variants
H., Kilic
Hürevren, Kılıç
Hurevren, Kilic
Kiliç H.
H.,Kilic
H.,Kılıç
H., Kılıç
K., Hürevren
K.,Hurevren
Kılıç,H.
Kilic,H.
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
Status
Website
https://www.atilim.edu.tr/tr/bilgisayar-muhendisligi-ana-bilim-dali/page/1620/akademik-kadro
ORCID ID
0000-0002-9058-0365
Scopus Author ID
Turkish CoHE Profile ID
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Sustainable Development Goals

5

GENDER EQUALITY
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0

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14

LIFE BELOW WATER
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0

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10

REDUCED INEQUALITIES
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3

GOOD HEALTH AND WELL-BEING
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1

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2

ZERO HUNGER
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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11

SUSTAINABLE CITIES AND COMMUNITIES
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8

DECENT WORK AND ECONOMIC GROWTH
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13

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4

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6

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2

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1

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15

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17

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1

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7

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12

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Scholarly Output

34

Articles

8

Views / Downloads

41/0

Supervised MSc Theses

10

Supervised PhD Theses

0

WoS Citation Count

66

Scopus Citation Count

63

WoS h-index

4

Scopus h-index

5

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0

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0

WoS Citations per Publication

1.94

Scopus Citations per Publication

1.85

Open Access Source

2

Supervised Theses

10

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JournalCount
Proceedings of the 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007 -- 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007 -- 21 February 2007 through 23 February 2007 -- Cairns -- 702542
International Journal of Engineering Education2
2014 IEEE Symposium on Intelligent Agents (IA) -- DEC 09-12, 2014 -- Orlando, FL1
21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS1
26th Annual International Symposium on Computer and Information Science -- SEP 26-28, 2011 -- Royal Soc London, London, ENGLAND1
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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
    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.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    CAWP A Combinatorial Auction Web Platform
    (Scitepress, 2010) Cereci, Ibrahim; Kilic, Hurevren
    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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    Conference Object
    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
    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.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 17
    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
    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.