Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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  • Conference Object
    Citation - Scopus: 1
    A Novel Feature To Predict Buggy Changes in a Software System
    (Springer, 2022) Yılmaz, Rahime; Nalçakan, Yağız; Haktanır, Elif
    Researchers have successfully implemented machine learning classifiers to predict bugs in a change file for years. Change classification focuses on determining if a new software change is clean or buggy. In the literature, several bug prediction methods at change level have been proposed to improve software reliability. This paper proposes a model for classification-based bug prediction model. Four supervised machine learning classifiers (Support Vector Machine, Decision Tree, Random Forrest, and Naive Bayes) are applied to predict the bugs in software changes, and performance of these four classifiers are characterized. We considered a public dataset and downloaded the corresponding source code and its metrics. Thereafter, we produced new software metrics by analyzing source code at class level and unified these metrics with the existing set. We obtained new dataset to apply machine learning algorithms and compared the bug prediction accuracy of the newly defined metrics. Results showed that our merged dataset is practical for bug prediction based experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • Book Part
    Citation - WoS: 299
    Citation - Scopus: 406
    Introduction To Machine Learning
    (Humana Press, 2014) Baştanlar, Yalın; Özuysal, Mustafa
    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.
  • Article
    Citation - WoS: 24
    Citation - Scopus: 31
    Dynamic Replication Strategies in Data Grid Systems: A Survey
    (Springer Verlag, 2015) Tos, Uras; Mokadem, Riad; Hameurlain, Abdelkader; Ayav, Tolga; Bora, Şebnem
    In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out.