Browsing by Author "Turhan, Burak."

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    Improving the performance of software defect predictors with internal and external information sources
    (Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2008., 2008.) Turhan, Burak.; Bener, Ayşe B.
    In this dissertation, we make an analysis of software defect prediction problem from a data mining perspective, where software characteristics are represented with static code features and defect predictors are learned from historical defect logs. We observe that straightforward applications of data mining methods for constructing defect predictors have reached a performance limit due to the limited information content in static code features. Therefore, we aim at increasing the information content in data without introducing new features, since collecting these may either be expensive or not possible in all contexts. We feed data mining methods with richer data in terms of information content. For this purpose, we propose the following methods: 1) relaxing the assumptions of data miners, 2) using project data from multiple companies, 3) modeling the interactions of software modules. For the first method, we use naive Bayes data miner and remove its i) independence and ii) equal importance of features assumptions. Then we compare the performance of defect predictors learned from local and remote data. Finally, we introduce call graph technique to model the interactions of modules. Our results on public industrial data show that: 1) relaxing the assumptions of naive Bayes may increase defect prediction performance significantly, 2) predictors learned from remote data have great capability of detecting defects at the cost of high false alarms, however this cost can be removed with the proposed filtering method 3) proposed way of modeling interactions may decrease the false alarm rates significantly. Our techniques provide guidelines for 1) employing defect prediction using remote information sources when local data are not available, 2) increasing prediction performances using local information sources.
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    Nonlinear dimensionality reduction methods for pattern recognition
    (Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2004., 2004.) Turhan, Burak.; Alpaydın, Ethem.
    The aim of dimensionality reduction is to find a lower dimensional, simpler representation while keeping the important information in the data. It is essential to employ dimensionality reduction for high dimensional data in order to extract relevant features and filter the non-relevant ones. This allows obtaining simpler models and useful knowledge from the data. In this thesis, we discuss and compare several unsupervised nonlinear methods for dimensionality reduction, namely, Isomap, Locally Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Curvilinear Distance Analysis (CDA) and Stochastic Neighbor Embedding (SNE), by testing their accuracies on standard benchmark data sets. We propose a modification (SNE-Iso Hybrid), and introduce the implicit learning of mapping functions in order to solve the problem of mapping previously unseen data points. We observe that using the metrics inherent in the data distribution allows better modeling than using the Euclidean distance and increases the model accuracies for nonlinear data.