Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Wirelless Mesh Network Throughput Analysis Using Petri Nets(Izmir Institute of Technology, 2022) Oğuzer, Lütfü Melih Buğra; Tuğlular, Tuğkan; Belli, FevziEvolving technology has made the understanding of quality perception in software processes more difficult. Unlike other sectors, rapid adaptation and software development processes have become a critical issue. This issue can especially be observed in the service, telecommunication, and high technology sectors. User demands and competition are quite high and with this competition, the need to subject the customized or developed software to rapid testing processes has formed. Undoubtedly, this process implies a great responsibility for the "quality assurance" teams. This responsibility has reached a level that can only be handled by the quality assurance departments that automate the testing cycles. However, it is also important that these cycles are very efficient. Our research is concerned with modeling test processes with Petri nets and creating test scenarios based on this modeling to make automation processes in the telecommunications industry more efficient. In this research, the performance analysis of wireless mesh networks is executed through place/transition petri-net modeling. Through this modeling, reusable test scenarios which were compared and analyzed with traditional automation processes were created for performance tests. The research also addresses another topic which is the shortening of the modeling processes created with Petri nets and how to make them more efficient. In this context, a tool has been developed in order to shorten the modeling process and analyze the reusable test scenarios. Finally, ten test engineers were interviewed about reusable test processes. In these interviews, feedback was provided on reusable test scenarios in test automation processes.Master Thesis Mutation Analysis of Specification-Based Contracts in Software Testing [master Thesis](01. Izmir Institute of Technology, 2021) Khalilov, Abbas; Tuğlular, Tuğkan; Belli, FevziSoftware used in fields such as medicine, finance, aviation and aerospace, nuclear power etc. is required to be reliable. Any software failures in these fields may have catastrophic consequences such as human and financial losses, which may cause a great damage to the economy and to social well-being. Hence, before launching, software should be rigorously tested. Testing can uncover the conditions, which software cannot handle. Those conditions might be overlooked during development. So, software testing points to the faults in the software under development to be patched. The important element of software testing is the use of the adequate test cases. If the outcome of the test case is positive, that means testing did not reveal any fault, then this test case might be considered as inefficient and useless for the tested version of software. Therefore, it is important to check test cases on adequacy, which can be achieved by mutation analysis. This thesis focuses on checking the adequacy of the test cases for Decision-Table-augmented Event Sequence Graphs (ESG-DTs) representation of a system under test by using mutation analysis. Test cases are represented in the Complete Event Sequence (CES) and Faulty CES (FCES) forms. This thesis presents a new set of mutation operators for mutation of contracts represented in Multi-Terminal Binary Decision Diagram (MTBDD). This thesis introduces a new approach for mutation of the ESG-DT model by using the proposed MTBDD mutation operators. The proposed approach is evaluated on three cases. The results for all cases show the drawback of specific FCES test sequences and the relationship between the mutant detection by CES/FCES sequences and proposed mutation operators.Master Thesis Application of Graph Neural Networks on Software Modeling(01. Izmir Institute of Technology, 2020) Leblebici, Onur Yusuf; Tuğlular, Tuğkan; Belli, FevziDeficiencies and inconsistencies introduced during the modeling of software systems can cause undesirable consequences that may result in high costs and negatively affect the quality of all developments made using these models. Therefore, creating better models will help the software engineers to build better software systems that meet expectations. One of the software modelling methods used for analysis of graphical user interfaces is Event Sequence Graphs (ESG). The goal of this thesis is to propose a method that predicts missing or forgotten links between events defined in an ESG via Graph Neural Networks (GNN). A five-step process consisting of the following steps is proposed: (i) data collection from ESG model, (ii) dataset transformation, (iii) GNN model training, (iv) validation of trained model and (v) testing the model on unseen data. Three performance metrics, namely cross entropy loss, area under curve and accuracy, were used to measure the performance of the GNN models. Examining the results of the experiments performed on different datasets and different variations of GNN, shows that even with relatively small datasets prepared from ESG models, predicts missing or forgotten links between events defined in an ESG can be achieved.