Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Demirörs, OnurCOAR Access Rights: search.filters.coarAccessRights.open access

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  • Master Thesis
    A Mutation-Based Approach To Alleviate the Class Imbalance Problem in Software Defect Prediction
    (01. Izmir Institute of Technology, 2023) Güner, Dinçer; Demirörs, Onur; Demirörs, Onur; Giray, Görkem
    Highly imbalanced training datasets considerably degrade the performance of software defect predictors. Software Defect Prediction (SDP) datasets have a general problem, which is class imbalance. Therefore, a variety of methods have been developed to alleviate Class Imbalance Problem (CIP). However, these classical methods, like data-sampling, balance datasets without connecting any relation with SDP. Over-sampling techniques generate synthetic minor class instances, which generalize a small number of minor class instances and result in less diverse instances, whereas under-sampling techniques eliminate major class instances, resulting in significant information loss. In this study, we present an approach that uses software mutations to balance software repositories. Mutation-based Approach (MBA) injects mutants into defect-free instances, causing them to transform into defective instances. In this way, MBA balances datasets with diverse data produced by mutation operators, and there is no loss on instances as in under-sampling. For recall scores, almost all rebalancing methods outperformed Baseline in Inter-release Defect Prediction (IRDP) scenario but only MBA significantly outperformed Baseline in Cross-project Defect Prediction (CPDP) scenario. The performance increase in recall resulted in the production of more false alarms. We can not generalize that MBA outperforms Baseline and the five over-sampling strategies in terms of AUC scores. In terms of recall values, the MBA performed better in CPDP than IRDP. For both IRDP and CPDP scenarios, there were significant and positive correlations between SMC (the change percentage of software measures) and recall, and SMC and false alarm but there was no significant correlation between SMC and AUC.
  • Master Thesis
    Blockchain Based Context Aware Access Control Structure Implementation for Security of Internet of Things System
    (01. Izmir Institute of Technology, 2022) Kul, Aslı; Demirörs, Onur; Erten, Yusuf Murat
    Nowadays, Internet of Things (IoT) devices, which have started to be included in our lives with the developing technology, are one of the popular working topics. While they are a means of transporting many important data in terms of usage areas, IoT devices have brought security concerns due to their being a new technology and technical limitations. The use of standard access control algorithms is insufficient for IoT environments due to their complexity and dynamism. In this study, considering the importance of sensitive critical data carried by IoT environments, Context Aware IoT Rule Based Access Control Algorithm, which is a proposed algorithm to ensure the security of interaction with IoT environments, is aimed to be integrated and used to create a reliable IoT environment by taking advantage of the security promising Blockchain technology. It is due to the use of distributed and cryptography methods that are widely used today.