Yazar "Okutan, Ahmet" seçeneğine göre listele

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    Bilingual software requirements tracing using vector space model
    (SciTePress, 2014) Yıldız, Olcay Taner; Okutan, Ahmet; Solak, Ercan
    In the software engineering world, creating and maintaining relationships between byproducts generated during the software lifecycle is crucial. A typical relation is the one that exists between an item in the requirements document and a block in the subsequent system design, i.e. class in the source code. In many software engineering projects, the requirement documentation is prepared in the language of the developers, whereas developers prefer to use the English language in the software development process. In this paper, we use the vector space model to extract traceability links between the requirements written in one language (Turkish) and the implementations of classes in another language (English). The experiments show that, by using a generic translator such as Google translate, we can obtain promising results, which can also be improved by using comment info in the source code.
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    Modeling a mobile value added services platform
    (Işık Üniversitesi, 2002-06) Okutan, Ahmet; Yarman, Bekir Sıddık Binboğa; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı
    Mobility is one of the new concepts has changed the way that people are accessing information. It is known that mobile phones can be used for more than just making phone calls. With the new generation mobile phones, mobile users can access a lot of Mobile Value Added Services (VAS). The term Value Added Services incorporates a lot of services ranging from popular information services like currency or weather information to entertainment services like multimedia messaging, logo, melody and picture message services. That is to say, a cellular phone owner can both request daily news, weather or horoscope information and can download multimedia messages, melodies, logos and picture messages to his mobile phone by sending a specific request message via SMS. In the meantime, Value Added Services Platforms utilize the GSM Operator's Short Message Service Centers, to provide Value Added Services to mobile phone users via SMS. Although the majority of Value Added Services are being provided via SMS, WAP and GPRS are other technologies that are utilized by VAS platforms. On the other hand, as the internet is making is possible to publish information content vie World Wide Web (www), mobile phones are becoming more and more popular to provide this information content to people who are mobile. So, in this context, VAS platforms are acting as mediums to provide infotainment to mobile subscribes from the web. This study models the technical features of sample Value Added Services Platform that works over SMS. The main system components of this modeled Value Added Service Platform are explained in detail. The important issues that needs special handling are described for each system component. Moreover, a research is made regarding the smart messaging which is one of the most important parts of VAS. For this, Nokia Smart Messaging Speciation, Nokia Multimedia Messaging Specifiation (MMS) and Enhanced Messaging Specifiation (EMS) are examined in detail. The modeled VAS Platform supports Nokia Smart Messaging Specification. Additionally it can support MMS and EMS if necessary software modules are implemented. So, the implemented software platform can send melodies, logos and picture messages can be requested by sending corresponding keywords for each service via SMS. In the remaining parts of the document, the word Platform will be used in stead of Value Added Services Platform (VASP) modeled in this study.
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    A novel kernel to predict software defectiveness
    (Elsevier Science Inc, 2016-09) Okutan, Ahmet; Yıldız, Olcay Taner
    Although the software defect prediction problem has been researched for a long time, the results achieved are not so bright. In this paper, we propose to use novel kernels for defect prediction that are based on the plagiarized source code, software clones and textual similarity. We generate precomputed kernel matrices and compare their performance on different data sets to model the relationship between source code similarity and defectiveness. Each value in a kernel matrix shows how much parallelism exists between the corresponding files of a software system chosen. Our experiments on 10 real world datasets indicate that support vector machines (SVM) with a precomputed kernel matrix performs better than the SVM with the usual linear kernel in terms of F-measure. Similarly, when used with a precomputed kernel, the k-nearest neighbor classifier (KNN) achieves comparable performance with respect to KNN classifier. The results from this preliminary study indicate that source code similarity can be used to predict defect proneness.
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    A novel regression method for software defect prediction with kernel methods
    (2013) Okutan, Ahmet; Yıldız, Olcay Taner
    In this paper, we propose a novel method based on SVM to predict the number of defects in the files or classes of a software system. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. The experiments on 10 Promise datasets indicate that SVM with a precomputed kernel performs as good as the SVM with the usual linear or RBF kernels in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities.
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    Software defect prediction using Bayesian networks
    (Springer, 2014-02) Okutan, Ahmet; Yıldız, Olcay Taner
    There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.
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    Software defect prediction using Bayesian networks and kernel methods
    (Işık Üniversitesi, 2012-07-01) Okutan, Ahmet; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Doktora Programı
    There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian modelling to determine the influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, We define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We wxtract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, We learn both the marginal defect proneness probability of the whole software system and the set of most effective metrics. Our experiments on nine open source Promise data repository data sets show that respense for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most efective metrics whereas coupling between objets (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less efective metris on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are unstustworthy. On tthe other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, We observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness.However, futher investigation involving a greater number of projects, is need to confirm our findings. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. Although the defect prediction problem haz been researched for a long time, the results achieved are not so bright. We use kernel programming to model the relationship between source code similarity and defectiveness. Each value in the kernel matrix shows how much parallelism exit between the corresponding files ib the kernel matrix shows how much parallelism exist between the corresponding files in the software system chosen. Our experiments on 10 real world datasets indicate that support vector machines (SVM) with a precalculated kernel matrix performs better than the SVM with the usual linear and RBF kernels and generates comparable results with the famous defect prediction methods like linear logistic regression and J48 in terms of the area under the curve (AUC).Furthermore, we observed that when the amount of similarity among the files of a software system is high, then the AUC found by the SVM with precomputed kernel can be used to predict the number of defects in the files or classes of a software system, because we observe a relationship between source code similarity and the number of defects. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities. The experiments on 10 Promise datasets indicate that while predicting the number of defects, SVM with a precomputed kernel performs as good as the SVM with the usual linear and RBF kernels, in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of these experiments suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software modules.