Browsing by Author "Yucalar F."

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    A case study for the software size estimation through MK II FPA and FP methods
    (Inderscience Publishers, 2016) Borandag E.; Yucalar F.; Erdogan S.Z.
    Software size estimation is one of the most crucial and daunting tasks for a software project manager. It is very important for the accurate planning and calculation of the software project. The importance of software size estimation becomes critical at the beginning of the software life cycle. Software size estimation is a basic input for the software effort and cost estimation. There are many different approaches and various methods for software size estimation up to now such as the function points method, e.g., IFPUG FPA, Mk II FPA, COSMIC FFP. In this paper, the size of the software project was calculated through the function points method and Mark II FPA. The same software project was implemented by different software development teams. Early estimations were performed for the software project and the data obtained as a result of the teams' studies were compared. Copyright © 2016 Inderscience Enterprises Ltd.
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    Majority vote feature selection algorithm in software fault prediction
    (ComSIS Consortium, 2019) Borandag E.; Ozcift A.; Kilinc D.; Yucalar F.
    Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance. © 2019, ComSIS Consortium. All rights reserved.
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    Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability
    (Elsevier B.V., 2020) Yucalar F.; Ozcift A.; Borandag E.; Kilinc D.
    Software development projects require a critical and costly testing phase to investigate efficiency of the resultant product. As the size and complexity of project increases, manual prediction of software defects becomes a time consuming and costly task. An alternative to manual defect prediction is the use of automated predictors to focus on faulty modules and let the software engineer to examine the defective part with more detail. In this aspect, improved fault predictors will always find a software quality application project to be applied on. There are many base predictors tested-designed for this purpose. However, base predictors might be combined with an ensemble strategy to further improve to increase their performance, particularly fault-detection abilities. The aim of this study is to demonstrate fault-prediction performance of ten ensemble predictors compared to baseline predictors empirically. In our experiments, we used 15 software projects from PROMISE repository and we evaluated the fault-detection performance of algorithms in terms of F-measure (FM) and Area under the Receiver Operating Characteristics (ROC) Curve (AUC). The results of experiments demonstrated that ensemble predictors might improve fault detection performance to some extent. © 2019 Karabuk University
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    Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Yucalar F.
    Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional (FR) and non-functional (NFR). Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual identification of these classes is a timewaster, and it needs to be automated. Methodically, machine learning (ML) approaches are applied to address this problem. In this study, twenty ML algorithms, such as Naïve Bayes, Rotation Forests, Convolutional Neural Networks, and transformers such as BERT, were used to predict FR and NFR. Any ML algorithm requires a dataset for training. For this goal, we generated a unique Turkish dataset having collected the requirements from real-world software projects with 4600 samples. The generated Turkish dataset was used to assess the performance of the three groups of ML algorithms in terms of F-score and related statistical metrics. In particular, out of 20 ML algorithms, BERTurk was found to be the most successful algorithm for discriminating FR and NFR in terms of a 95% F-score metric. From the FR and NFR identification problem point of view, transformer algorithms show significantly better performances. © 2023 by the author.