Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Now showing 1 - 10 of 15
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    The Relation Between Bug Fix Change Patterns and Change Impact Analysis
    (Institute of Electrical and Electronics Engineers, 2021) Ufuktepe,E.; Tuglular,T.; Palaniappan,K.
    Change impact analysis analyzes the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. In this study, we analyze the bug fix change patterns to have a better understanding of what types of changes are common in fixing bugs. To achieve this, we implemented a tool that compares two versions of codes and detects the changes that are made. Then, we investigated how these changes are related to change impact analysis. In our case study, we used 13 of the projects and 621 bugs from Defects4J to identify the common change types in bug fixed. Then, to find the change types related to cause an impact in the software, we performed an impact analysis on a subset of projects and bugs of Defects4J. The results have shown that, on average, 90% of the bug fix change types are adding a new method declaration and changing the method body. Then, we investigated if these changes cause an impact or a ripple effect in the software by performing a Markov chain-based change impact analysis. The results show that the bug fix changes had only impact rates within a range of 0.4%-5%. Furthermore, we performed a statistical correlation analysis to find if any of the bug fixes have a significant correlation on the impact of change. The results have shown that there is a negative correlation between caused impact with the change types adding new method declaration and changing method body. On the other hand, we found that there is a positive correlation between caused impact and changing the field type. © 2021 IEEE.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Integrative Biological Network Analysis To Identify Shared Genes in Metabolic Disorders
    (Institute of Electrical and Electronics Engineers, 2022) Tenekeci, Samet; Işık, Zerrin
    Identification of common molecular mechanisms in interrelated diseases is essential for better prognoses and targeted therapies. However, complexity of metabolic pathways makes it difficult to discover common disease genes underlying metabolic disorders; and it requires more sophisticated bioinformatics models that combine different types of biological data and computational methods. Accordingly, we built an integrative network analysis model to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks by combining gene expression, protein-protein interaction, and gene ontology data from multiple sources. For 90 different configurations of disease networks, we detected the significant modules by using MCL, SPICi, and Linkcomm graph clustering algorithms. We also performed a comparative evaluation on disease modules to determine the best method providing the highest biological validity. By overlapping the disease modules, we identified 22 shared genes for MS-CAD and T2D-CAD. Moreover, 19 out of these genes were directly or indirectly associated with relevant diseases in the previous medical studies. This study does not only demonstrate the performance of different biological data sources and computational methods in disease-gene discovery, but also offers potential insights into common genetic mechanisms of the metabolic disorders.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Cauchy-Rician Model for Backscattering in Urban Sar Images
    (Institute of Electrical and Electronics Engineers, 2022) Karakuş, Oktay; Kuruoğlu, Ercan Engin; Achim, Alin; Altınkaya, Mustafa Aziz
    This letter presents a new statistical model for urban scene synthetic aperture radar (SAR) images by combining the Cauchy distribution, which is heavy tailed, with the Rician backscattering. The literature spans various well-known models most of which are derived under the assumption that the scene consists of multitudes of random reflectors. This idea specifically fails for urban scenes since they accommodate a heterogeneous collection of strong scatterers such as buildings, cars, and wall corners. Moreover, when it comes to analyzing their statistical behavior, due to these strong reflectors, urban scenes include a high number of high amplitude samples, which implies that urban scenes are mostly heavy-tailed. The proposed Cauchy-Rician model contributes to the literature by leveraging nonzero location (Rician) heavy-tailed (Cauchy) signal components. In the experimental analysis, the Cauchy-Rician model is investigated in comparison to state-of-the-art statistical models that include $\mathcal {G}_{0}$ , generalized gamma, and the lognormal distribution. The numerical analysis demonstrates the superior performance and flexibility of the proposed distribution for modeling urban scenes.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 12
    Event Oriented Vs Object Oriented Analysis for Microservice Architecture: an Exploratory Case Study
    (Institute of Electrical and Electronics Engineers, 2021) Ünlü, Hüseyin; Tenekeci, Samet; Yıldız, Ali; Demirörs, Onur
    The rapidly developing internet infrastructure together with the advances in software technology has enabled the development of cloud-based modern web applications that are much more responsive, flexible, and reliable compared to traditional monolithic applications. Such modern applications require new software design paradigms and architectures. Microservice-based architecture (MSbA), which aims to create small, isolated, loosely-coupled applications that work in cohesion, becoming widespread as one of these approaches. MSbA allows the developed applications to be deployed and maintained separately, as well as scaled on demand. However, there is no de facto method for the analysis and design of systems for these architectures. In this paper, we compared the usefulness of the object-oriented (OO) and event-oriented (EO) approaches for analyzing and designing MS-based systems. More specifically, we performed an exploratory case study to analyze, design, and implement a software application dealing with the 'application and evaluation process of graduate students at IzTech'. This paper discusses the results of this case study. We observe that the EO approaches have significant advantages with respect to the OO approaches.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 4
    Çok-etiketli Film Türü Sınıflandırması için Türkçe Konu Modellemesi Veri Kümesi
    (Institute of Electrical and Electronics Engineers, 2020) Jabrayilzade, Elgün; Poyraz Arslan, Algın; Para, Hasan; Polatbilek, Ozan; Sezerer, Erhan; Tekir, Selma
    Statistical topic modeling aims to assign topics to documents in an unsupervised way. Latent Dirichlet Allocation (LDA) is the standard model for topic modeling. It shows good performance on document collections, documents being relatively long texts but it has poor performance on short texts. Topic modeling on short texts is on the rise due to the potential of social media. Thus, approaches that are able to nd topics on short texts as well as long texts are sought. However, there is a lack of datasets that include both long and short texts which have the same ground-truth categories. In this work, we release a Turkish movie dataset which contain both short lm descriptions and long subscripts where lm genre can be considered as topic. Furthermore, we provide multi-label movie genre classication results using a Feed Forward Neural Network (FFNN) taking LDA document-topic or Doc2Vec dense representations. © 2020 IEEE.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 5
    Code Change Sniffer: Predicting Future Code Changes With Markov Chain
    (Institute of Electrical and Electronics Engineers, 2021) Ufuktepe, Ekincan; Tuğlular, Tuğkan
    Code changes are one of the essential processes of software evolution. These changes are performed to fix bugs, improve quality of software, and provide a better user experience. However, such changes made in code could lead to ripple effects that can cause unwanted behavior. To prevent such issues occurring after code changes, code change prediction, change impact analysis techniques are used. The proposed approach uses static call information, forward slicing, and method change information to build a Markov chain, which provides a prediction for code changes in the near future commits. For static call information, we utilized and compared call graph and effect graph. We performed an evaluation on five open-source projects from GitHub that varies between 5K-26K lines of code. To measure the effectiveness of our proposed approach, recall, precision, and f-measure metrics have been used on five open-source projects. The results show that the Markov chain that is based on call graph can have higher precision compared to effect graph. On the other hand, for small number of cases higher recall values are obtained with effect graph compared to call graph. With a Markov chain model based on call graph and effect graph, we can achieve recall values between 98%-100%. © 2021 IEEE.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 6
    Truth Ratios of Syllogistic Moods
    (Institute of Electrical and Electronics Engineers, 2015) Zarechnev, Mikhail; Kumova, Bora İsmail
    The syllogistic system consists of 256 moods, of which only 24 have been recognized as true. From a set-theoretical point of view, a mood can be represented with three sets and their possible relationships. Three sets can have up to seven sub-sets or spaces. In an earlier work we have used 41 permutations of the spaces, out of which every mood matches an individual number as true or false cases. The truth ratio of a mood is then calculated, by relating the true and false cases with each other. In this work we revise the previously presented properties of the moods and the syllogistic system, this time by using the maximum possible cover, which consists of 96 distinct space permutations. Our results mostly verify our previous findings, like the additional true mood anasoy, the inherently symmetric truth distribution of the moods. Additionally we have revealed some new properties, like the equivalence of some moods, which reduces the system to 136 distinct moods.
  • Conference Object
    Citation - WoS: 10
    Citation - Scopus: 12
    Random Test Generation From Regular Expressions for Graphical User Interface (gui) Testing
    (Institute of Electrical and Electronics Engineers, 2019) Kılınççeker, Onur; Silistre, Alper; Challenger, Moharram; Belli, Fevzi
    Generation of test sequences, that is, (user) inputs - expected (system) outputs, is an important task of testing of graphical user interfaces (GUI). This work proposes an approach to randomly generate test sequences that might he used for comparison with existing GUI testing techniques to evaluate their efficiency. The proposed approach first models CUI under test by a finite state machine (FSM) and then converts it to a regular expression (RE). A tool based on a special technique we developed analyzes the RE to fulfill missing context information such as the position of a symbol in the RE. The result is a context table representing the RE. The proposed approach traverses the context table to generate the test sequences. To do this, the approach repeatedly selects a symbol in the table, starting from the initial symbol, in a random manner until reaching a special, finalizing symbol for constructing a test sequence. Thus, the approach uses a symbol coverage criterion to assess the adequacy of the test generation. To evaluate the approach, mutation testing is used. The proposed technique is to a great extent implemented and is available as a tool called PQ-Ran Test (PQ-analysis based Random Test Generation). A case study demonstrates the proposed approach and analyzes its effectiveness by mutation testing.
  • Conference Object
    Citation - Scopus: 5
    Community Detection in Model-Based Testing To Address Scalability: Study Design
    (Institute of Electrical and Electronics Engineers, 2020) Silistre, Alper; Kılınççeker, Onur; Belli, Fevzi; Challenger, Moharram; Kardaş, Geylani
    Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently. In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms. A case study is presented as an example of possible improvements to make a model-based testing approach more efficient. We demonstrate layered ESG models as an example of our approach to consider the scalability problem. We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied. © 2020 Polish Information Processing Society - as it is since 2011.
  • Conference Object
    Citation - WoS: 7
    Citation - Scopus: 8
    Models in Graphical User Interface Testing: Study Design
    (Institute of Electrical and Electronics Engineers, 2020) Silistre, Alper; Kılınççeker, Onur; Belli, Fevzi; Challenger, Moharram; Kardaş, Geylani
    Model-based GUI testing is an important concept in Software GUI testing. Manual testing is a time-consuming labor and heavily error-prone. It has several well-accepted models that Software Testing community has been working and contributing to them for many years. This paper reviews different models used in model-based GUI testing and presents a case study with a proposed approach for how to convert several well-accepted models to ESG (Event Sequence Graphs) to generate test cases and execute them with an aim to consolidate past and future works in a single model. © 2020 IEEE.