Browsing by Author "Kilinc, D"

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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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    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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    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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    A spark-based big data analysis framework for real-time sentiment prediction on streaming data
    Kilinc, D
    There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real-time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real-time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naive Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real-time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real-time modes are 86.77% and 80.93%, respectively.