Browsing by Author "Borandağ E."

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    Multi-level reranking approach for bug localization
    (Blackwell Publishing Ltd, 2016) Kılınç D.; Yücalar F.; Borandağ E.; Aslan E.
    Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi-level re-ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi-level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets. © 2016 Wiley Publishing Ltd
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    Development of majority vote ensemble feature selection algorithm augmented with rank allocation to enhance Turkish text categorization
    (Turkiye Klinikleri, 2021) Borandağ E.; Özçift A.; Kaygusuz Y.
    The increase in the number of texts as digital documents from numerous sources such as customer reviews, news, and social media has made text categorization crucial in order to be able to manage the enormous amount of data. The high dimensional nature of these texts requires a preliminary feature selection task to reduce the feature space with a potential increase in the prediction accuracy. In this study, we developed an ensemble feature selection method, namely majority vote rank allocation, was developed for Turkish text categorization purposes. The method uses a majority voting ensemble strategy in combination with a rank allocation approach to combine weak filters such as information gain, symmetric uncertainty, relief, and correlation-based feature selection. Thus, the proposed method measures the quality of the features among all features with the majority votes of the filters and ranking allocation. The feature selection efficacy of the method was tested on two datasets, one from the literature and a newly collected dataset. The effect of the obtained features on the classification prediction performance was evaluated on top of the naive bayes, support vector machine J48, and random forests algorithms. It was empirically observed that the developed method improved the prediction accuracies of the classifiers compared to the mentioned filters. The statistical significance of the experimental results were also validated with the use of a two-way analysis of variance test. © TÜBİTAK
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    LSRM: A New Method for Turkish Text Classification
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Borandağ E.
    The text classification method is one of the most frequently used approaches in text mining studies. Text classification requires a model generation using a predefined dataset, and this model aims to assign uncategorized data to a correct category. In line with this purpose, this study used machine learning algorithms, deep learning algorithms, word embedding algorithms, and transfer-learning algorithms to classify Turkish texts using three diverse datasets, one of which is new, to analyze text classification performances for the Turkish language. The preparation process of the newly added dataset involved the variations in Turkish word usage patterns over the years, since it consisted of timestamp-enabled data. The study also developed a novel method named LSRM to increase the text classification performance for agglutinative languages such as Turkish. After testing the new method on datasets, the statistical ANOVA method revealed that applying the proposed LSRM method increased the classification performance. © 2024 by the author.