Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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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. AuthorConference Object Citation - WoS: 3Citation - Scopus: 2Heterogeneous Modeling and Testing of Software Product Lines(IEEE, 2021) Belli, Fevzi; Tuğlular, Tuğkan; Ufuktepe, EkincanSoftware product line (SPL) engineering is a widely accepted approach to systematically realizing software reuse in an industrial environment. Feature models, a centerpiece of most SPL engineering techniques, are appropriate to model the variability and the structure of SPLs, but not their behavior. This paper uses the idea to link feature modeling to model-based behavior modeling and to determine the test direction (top-down or bottom-up) based on the variability binding. This heterogeneous modeling enables a holistic system testing for validating both desirable (positive) and undesirable (negative) properties of the SPL and variants. The proposed approach is validated by a non-trivial example and evaluated by comparison.Conference Object Citation - Scopus: 1Mutation Analysis of Specification-Based Contracts in Software Testing [conference Object](IEEE, 2021) Khalilov, Abbas; Tuğlular, Tuğkan; Belli, FevziThis work focuses on checking the adequacy of the test cases generated using Decision-Table-augmented Event Sequence Graphs (ESG-DTs), which represents the specification of a system under test, by using mutation analysis. Test cases are represented in the Complete Event Sequence (CES) and Faulty CES (FCES) forms. We present a new set of mutation operators for mutation of contracts represented in Multi-Terminal Binary Decision Diagram (MTBDD) and introduce a new approach to mutation of the ESG-DT model by using the proposed mutation operators. The approach is evaluated on three cases. The results show the drawback of specific FCES test sequences and the relationship between the mutant detection by CES/FCES sequences and proposed mutation operators.