Browsing by Author "Borandag, E"

Now showing 1 - 14 of 14
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    Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability
    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. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.
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    Algorithm of Neighbor Isolated Scattering Number
    Tosun, MA; Aslan, E; Borandag, E
    Network security and reliability is an essential part of computer networks. Network security has had to improve due to hackers. Business continuity is another reason for improvement. Networks can be modeled with graphs. Various parameters exist to measure the vulnerability of graphs, and hence of networks. In this paper, we consider neighbor isolated scattering number and an algorithm has been developed for the proposed vulnerability measurement parameter, which we recommend to measure for any graph and the algorithm is analyzed by software code metrics and have been shown to be useful. Thus, it has been concluded that humanpower will be saved by using an algorithm while measuring vulnerability for any graph.
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    A hybrid approach based on deep learning for gender recognition using human ear images
    Karasulu, B; Yücalar, F; Borandag, E
    Nowadays, the use of the human ear images gains importance for the sustainability of biometric authorization and surveillance systems. Contemporary studies show that such processes can be done semi-automatically or fully automatically, instead of being done manually. Due to the fact that deep learning uses abstract features (i.e., representation learning), it reaches quite high performance values compared to classical methods. In our study, a synergistic gender recognition approach based on hybrid deep learning was created based on the use of human ear images in classifying people fully automatically according to their gender. By means of hybridization, hybrid deep neural network architectural models are used, which include both convolutional neural network component and recurrent neural network type components together. In these models, long-short term memory and gated recurrent unit are taken as recurrent neural network type components. Thanks to these components, the hybrid model extracts the relational dependencies between the pixel regions in the image very well. On account of this synergistic approach, the gender classification accuracy of hybrid models is higher than the standalone convolutional neural network model in our study. Two different image datasets with gender marking were used in our experiments. The reliability of the experimental results has been proven by objective metrics. In the conducted experiments, the highest values in gender recognition with hybrid models were obtained with the test accuracy of 85.16% for the EarVN dataset and 87.61% for the WPUT dataset, respectively. Discussion and conclusions are included in the last section of our study.
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    A case study for the software size estimation through MK II FPA and FP methods
    Borandag, E; Yucalar, F; Erdogan, SZ
    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.
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    Multi-level reranking approach for bug localization
    Kilinç, D; Yücalar, F; Borandag, 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.
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    Regression Analysis Based Software Effort Estimation Method
    Yücalar, F; Kilinc, D; Borandag, E; Ozcift, A
    Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.
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    Development of majority vote ensemble feature selection algorithm augmented with rank allocation to enhance Turkish text categorization
    Borandag, 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.
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    Majority Vote Feature Selection Algorithm in Software Fault Prediction
    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.
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    Software Fault Prediction Using an RNN-Based Deep Learning Approach and Ensemble Machine Learning Techniques
    Borandag, E
    Alongside the modern software development life cycle approaches, software testing has gained more importance and has become an area researched actively within the software engineering discipline. In this study, machine learning and deep learning-related software fault predictions were made through a data set named SFP XP-TDD, which was created using three different developed software projects. A data set of five different classifiers widely used in the literature and their Rotation Forest classifier ensemble versions were trained and tested using this data set. Numerous publications in the literature discussed software fault predictions through ML algorithms addressing solutions to different problems. Some of these articles indicated the usage of feature selection algorithms to improve classification performance, while others reported operating ensemble machine learning algorithms for software fault predictions. Besides, a detailed literature review revealed that there were few studies involving software fault prediction with DL algorithms due to the small sample sizes in the data sets and the low success rates in the tests performed on these datasets. As a result, the major contribution of this research was to statistically demonstrate that DL algorithms outperformed ML algorithms in data sets with large sample values via employing three separate software fault prediction datasets. The experimental outcomes of a model that includes a layer of recurrent neural networks (RNNs) were enclosed within this study. Alongside the aforementioned and generated data sets, the study also utilized the Eclipse and Apache Active MQ data sets in to test the effectiveness of the proposed deep learning method.
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    Vehicle Plate Tracking System
    Özbaysar, E; Borandag, E
    Image processing field has been improved thanks to various technology in recent years. One of them is a very significant program OpenCv technology. OpenCv is an open source coded independent image processing library and can work on multiple free platforms. On the other hand, expect for image processing technology through Internet of Things (IOT) along with Industry 4.0. had very huge development in this field. There are hundreds of different devices which were developed for this area. One of these devices is the Raspberry PI tool. In this study, a platform for vehicle license plate tracking system which uses embedded system was developed. It has been aimed at making push notification for relevant units by comparing an image with image processing algorithm by means of camera, screen and GPRS modul which were added on this developed system, Raspberry PI, with the current notifications.
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    A Blockchain-Based Recycling Platform Using Image Processing, QR Codes, and IoT System
    Borandag, E
    The climate crisis is one of the most significant challenges of the twenty-first century. The primary cause of high carbon emissions is industrial production that relies on carbon-based energy sources such as fuel oil, paraffin, coal, and natural gas. One of the effective methods to minimize carbon emissions originating from the use of energy resources is using recycling systems. A blockchain-based recycling platform was developed in this regard, adhering to the basic principles of Industry 4.0, which Robert Bosch GmbH and Henning Kagermann's working group described as an industrial strategy plan at the Hannover Fair in 2013. Concurrently, the recycling platform has set up an infrastructure that combines blockchain, AI, and IoT technologies for recycling objects. An IoT-based smart device was developed to collect recyclable objects. Thanks to the embedded artificial intelligence software and QR code sensor on the device, recyclable objects can be collected in different hoppers. In the laboratory studies, correct object recognition success was achieved at a rate of 98.2%.
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    THE AVERAGE SCATTERING NUMBER OF GRAPHS
    Aslan, E; Kilinç, D; Yücalar, F; Borandag, E
    The scattering number of a graph is a measure of the vulnerability of a graph. In this paper we investigate a refinement that involves the average of a local version of the parameter. If v is a vertex in a connected graph G, then sc(v)(G) = max{omega(G - S-v) - vertical bar S-v vertical bar},where the maximum is taken over all disconnecting sets S-v of G that contain v. The average scattering number of G denoted by sc(av)(G), is defined as sc(av)(G) = Sigma v is an element of V(G)sc(v)(G)/n, where n will denote the number of vertices in graph G. Like the scattering number itself, this is a measure of the vulnerability of a graph, but it is more sensitive. Next, the relations between average scattering number and other parameters are determined. The average scattering number of some graph classes are obtained. Moreover, some results about the average scattering number of graphs obtained by graph operations are given.
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    LSRM: A New Method for Turkish Text Classification
    Borandag, 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.
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    TTC-3600: A new benchmark dataset for Turkish text categorization
    Kilinç, D; Özçift, A; Bozyigit, F; Yildirim, P; Yücalar, F; Borandag, E
    Owing to the rapid growth of the World Wide Web, the number of documents that can be accessed via the Internet explosively increases with each passing day. Considering news portals in particular, sometimes documents related to categories such as technology, sports and politics seem to be in the wrong category or documents are located in a generic category called others. At this point, text categorization (TC), which is generally addressed as a supervised learning task is needed. Although there are substantial number of studies conducted on TC in other languages, the number of studies conducted in Turkish is very limited owing to the lack of accessibility and usability of datasets created. In this paper, a new dataset named TTC-3600, which can be widely used in studies of TC of Turkish news and articles, is created. TTC-3600 is a well-documented dataset and its file formats are compatible with well-known text mining tools. Five widely used classifiers within the field of TC and two feature selection methods are evaluated on TTC-3600. The experimental results indicate that the best accuracy criterion value 91.03% is obtained with the combination of Random Forest classifier and attribute ranking-based feature selection method in all comparisons performed after pre-processing and feature selection steps. The publicly available TTC-3600 dataset and the experimental results of this study can be utilized in comparative experiments by other researchers.