Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Link Prediction for Completing Graphical Software Models Using Neural Networks(IEEE, 2023) Leblebici, Onur; Tuğlular, Tuğkan; Belli, FevziDeficiencies and inconsistencies introduced during the modeling of software systems may result in high costs and negatively impact the quality of all developments performed using these models. Therefore, developing more accurate models will aid software architects in developing software systems that match and exceed expectations. This paper proposes a graph neural network (GNN) method for predicting missing connections, or links, in graphical models, which are widely employed in modeling software systems. The proposed method utilizes graphs as allegedly incomplete, primitive graphical models of the system under consideration (SUC) as input and proposes links between its elements through the following steps: (i) transform the models into graph-structured data and extract features from the nodes, (ii) train the GNN model, and (iii) evaluate the performance of the trained model. Two GNN models based on SEAL and DeepLinker are evaluated using three performance metrics, namely cross-entropy loss, area under curve, and accuracy. Event sequence graphs (ESGs) are used as an example of applying the approach to an event-based behavioral modeling technique. Examining the results of experiments conducted on various datasets and variations of GNN reveals that missing connections between events in an ESG can be predicted even with relatively small datasets generated from ESG models. AuthorCorrection Corrections To “massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization”(IEEE, 2023) Yılmaz, Saadet Simay; Özbek, Berna[No abstract available]Article Citation - WoS: 3Citation - Scopus: 4Application of the Law of Minimum and Dissimilarity Analysis To Regression Test Case Prioritization(IEEE, 2023) Ufuktepe, Ekincan; Tuğlular, TuğkanRegression testing is one of the most expensive processes in testing. Prioritizing test cases in regression testing is critical for the goal of detecting the faults sooner within a large set of test cases. We propose a test case prioritization (TCP) technique for regression testing called LoM-Score inspired by the Law of Minimum (LoM) from biology. This technique calculates the impact probabilities of methods calculated by change impact analysis with forward slicing and orders test cases according to LoM. However, this ordering doesn't consider the possibility that consecutive test cases may be covering the same methods repeatedly. Thereby, such ordering can delay the time of revealing faults that exist in other methods. To solve this problem, we enhance the LoM-Score TCP technique with an adaptive approach, namely with a dissimilarity-based coordinate analysis approach. The dissimilarity-based coordinate analysis uses Jaccard Similarity for calculating the similarity coefficients between test cases in terms of covered methods and the enhanced technique called Dissimilarity-LoM-Score (Dis-LoM-Score) applies a penalty with respective on the ordered test cases. We performed our case study on 10 open-source Java projects from Defects4J, which is a dataset of real bugs and an infrastructure for controlled experiments provided for software engineering researchers. Then, we hand-seeded multiple mutants generated by Major, which is a mutation testing tool. Then we compared our TCP techniques LoM-Score and Dis-LoM-Score with the four traditional TCP techniques based on their Average Percentage of Faults Detected (APFD) results.Article Citation - WoS: 16Citation - Scopus: 25A Privacy-Preserving Scheme for Smart Grid Using Trusted Execution Environment(IEEE, 2023) Akgün, Mete; Üstündağ Soykan, Elif; Soykan, GürkanThe increasing transformation from the legacy power grid to the smart grid brings new opportunities and challenges to power system operations. Bidirectional communications between home-area devices and the distribution system empower smart grid functionalities. More granular energy consumption data flows through the grid and enables better smart grid applications. This may also lead to privacy violations since the data can be used to infer the consumer's residential behavior, so-called power signature. Energy utilities mostly aggregate the data, especially if the data is shared with stakeholders for the management of market operations. Although this is a privacy-friendly approach, recent works show that this does not fully protect privacy. On the other hand, some applications, like nonintrusive load monitoring, require disaggregated data. Hence, the challenging problem is to find an efficient way to facilitate smart grid operations without sacrificing privacy. In this paper, we propose a privacy-preserving scheme that leverages consumer privacy without reducing accuracy for smart grid applications like load monitoring. In the proposed scheme, we use a trusted execution environment (TEE) to protect the privacy of the data collected from smart appliances (SAs). The scheme allows customer-oriented smart grid applications as the scheme does not use regular aggregation methods but instead uses customer-oriented aggregation to provide privacy. Hence the accuracy loss stemming from disaggregation is prevented. Our scheme protects the transferred consumption data all the way from SAs to Utility so that possible false data injection attacks on the smart meter that aims to deceive the energy request from the grid are also prevented. We conduct security and game-based privacy analysis under the threat model and provide performance analysis of our implementation. Our results demonstrate that the proposed method overperforms other privacy methods in terms of communication and computation cost. The execution time of aggregation for 10,000 customers, each has 20 SAs is approximately 1 second. The decryption operations performed on the TEE have a linear complexity e.g., 172800 operations take around 1 second while 1728000 operations take around 10 seconds. These results can scale up using cloud or hyper-scalers for real-world applications as our scheme performs offline aggregation.Article Citation - WoS: 2Citation - Scopus: 6Incremental Testing in Software Product Lines-An Event Based Approach(IEEE, 2023) Beyazıt, Mutlu; Tuğlular, Tuğkan; Öztürk Kaya, DilekOne way of developing fast, effective, and high-quality software products is to reuse previously developed software components and products. In the case of a product family, the software product line (SPL) approach can make reuse more effective. The goal of SPLs is faster development of low-cost and high-quality software products. This paper proposes an incremental model-based approach to test products in SPLs. The proposed approach utilizes event-based behavioral models of the SPL features. It reuses existing event-based feature models and event-based product models along with their test cases to generate test cases for each new product developed by adding a new feature to an existing product. Newly introduced featured event sequence graphs (FESGs) are used for behavioral feature and product modeling; thus, generated test cases are event sequences. The paper presents evaluations with three software product lines to validate the approach and analyze its characteristics by comparing it to the state-of-the-art ESG-based testing approach. Results show that the proposed incremental testing approach highly reuses the existing test sets as intended. Also, it is superior to the state-of-the-art approach in terms of fault detection effectiveness and test generation effort but inferior in terms of test set size and test execution effort.Article Citation - WoS: 15Citation - Scopus: 17Massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization(IEEE, 2023) Yilmaz, Saadet Simay; Özbek, BernaMobile edge computing (MEC) has been considered a promising technology to reduce task offloading and computing delay by enabling mobile devices to offload their computation-intensive tasks. Non-orthogonal multiple access (NOMA) is regarded as a promising method of increasing spectrum efficiency, while Massive multiple-input multiple-output (MIMO) can support a larger number of users for simultaneous offloading. These two technologies can effectively facilitate offloading and further improve the performance of MEC systems. In this work, we propose a NOMA and Massive MIMO assisted MEC system for delay-sensitive applications. Our objective is to minimize the overall computing and transmission delay under users' transmit power and MEC computing capability. Through the pairing scheme for Massive MIMO-NOMA, the users with the higher channel gain can offload all their data, while the users with the lower channel gain can offload a portion of their data to the MEC. Performance results are provided regarding to the sum data rate and overall system delay compared with the orthogonal multiple access (OMA)-MIMO based and Massive MIMO (M-MIMO) based MEC systems.Article Citation - WoS: 3Citation - Scopus: 3A Domain-Specific Language for the Document-Based Model-Driven Engineering of Business Applications(IEEE, 2022) Leblebici, Onur; Kardaş, Geylani; Tuğlular, TuğkanTo facilitate the development of business applications, a domain-specific language (DSL), called DARC, is introduced in this paper. Business documents including the descriptions of the responsibilities, authorizations, and collaborations, are used as the first-class entities during model-driven engineering (MDE) with DARC. Hence the implementation of the business applications can be automatically achieved from the corresponding document models. The evaluation of using DARC DSL for the development of commercial business software was performed in an international sales, logistics, and service solution provider company. The results showed that the code for all business documents and more than 50% of the responsibility descriptions composing the business applications could be generated automatically by modeling with DARC. Finally, according to the users' feedback, the assessment clearly revealed the adoption of DARC features in terms of the DSL quality characteristics, namely functional suitability, usability, reliability, maintainability, productivity, extensibility, compatibility, and expressiveness.Article Citation - WoS: 8Citation - Scopus: 14An End-To Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things(IEEE, 2021) Nakıp, Mert; Karakayalı, Kubilay; Güzeliş, Cüneyt; Rodoplu, VolkanWe develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection, forecasting) technique pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic, automatic feature selection capability. In addition, we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.Article Citation - WoS: 6Citation - Scopus: 8Flexible and Expandable Robot for Tissue Therapies - Modeling and Design(IEEE, 2021) Atwya, Mohamed; Kavak, Can; Alisse, Elodie; Liu, YanQiang; Damian, Dana D.Objective: Implantable technologies should be mechanically compliant with the tissue in order to maximize tissue quality and reduce inflammation during tissue reconstruction. We introduce the development of a flexible and expandable implantable robotic (FEIR) device for the regenerative elongation of tubular tissue by applying controlled and precise tension to the target tissue while minimizing the forces produced on the surrounding tissue. Methods: We introduce a theoretical framework based on iterative beam theory static analysis for the design of an expandable robot with a flexible rack. The model takes into account the geometry and mechanics of the rack to determine a trade-off between its stiffness and capability to deliver the required tissue tension force. We empirically validate this theory on the benchtop and with biological tissue. Results: We show that FEIR can apply the required therapeutical forces on the tissue while reducing the amount of force it applies to the surrounding tissues as well as reducing self-damage. Conclusion: The study demonstrates a method to develop robots that can change size and shape to fit their dynamic environment while maintaining the precision and delicacy necessary to manipulate tissue by traction. Significance: The method is relevant to designers of implantable technologies. The robot is a precursor medical device for the treatment of Long-Gap Esophageal Atresia and Short Bowel Syndrome.Article Citation - WoS: 6Citation - Scopus: 6User Selection for Millimeter Wave Non-Uniform Full Dimensional Mimo(IEEE, 2020) Mumtaz, Rao; Gonzalez, Jonathan; Cumalı, İrem; Özbek, BernaThe millimeter wave (mmWave) based full-dimensional (FD) MIMO communication is one of the promising technology to fulfill the demand of high data rate for the sixth generation (6G) services including 6D hologram, haptic and multi-sensory communications. In order to satisfy the requirements of 6G applications, we investigate a non-uniform rectangular array (NURA) structure with FD-MIMO antenna systems for the multiuser mmWave communications. For the dense scenarios where the number of users to be served is high, we propose user selection algorithms for both digital and hybrid transceiver designs in FD-MIMO with NURA for the multiuser mmWave communications. For the digital transceivers, the users are selected based on their channel correlation considering FD-MIMO with NURA structures. For the hybrid transceivers, sequential user and beam selection is performed using the correlation between the beamspace channels in FD-MIMO with NURA case. The superiority of the NURA compared to uniform antenna structure is shown through the performance evaluations in the multiuser mmWave communications. Besides, the sum data rate results and complexity analysis denote the feasibility of the proposed algorithms compared to the joint user and beam selection schemes.