Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Effectiveness of Using Clustering for Test Case Prioritization(Izmir Institute of Technology, 2019) Günel, Can; Ayav, Tolga; Ayav, TolgaSoftware testing is one of the most important processes in the software development life cycle. As software evolves, previous test cases need to be re-executed to make sure that there is no new bugs introduced and nothing is broken in the existing behaviours. However, re-execution of all test cases could be expensive. That is why, test case prioritization method can be used to detect faults earlier by prioritizing the test cases which could have the higher possibility than others to find faults. Studying different approaches, implementing different techniques or putting these techniques to test on different programs could make it easier to answer which technique should be used for which kind of programs or faults. We address this issue, focusing on selecting different test case prioritization approaches and calculating the average fault detection ratios of prioritized test suites. As a novelty, we propose to perform an optimization algorithm on one of the approaches called `Clustering` to increase its efficiency. To do that, our main objective is determined as maximizing the distance between each clusters by using the coverage information. The distance is measured as the difference of covered functions of test cases in a test suite. In the end, this study will give a hint about selection of test case prioritization technique to be used by checking the empirical results of the experiments.Master Thesis Event Distortion Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks(Izmir Institute of Technology, 2017) Al-Qamaji, Ali Mudheher Raghib Kafi; Atakan, Barış; Atakan, BarışWireless Sensor Network (WSN) is a set of inexpensive densely deployed wireless sensor nodes with limited functionalities and scarcity in energies, whose observations are forwarded or relayed by intermediate nodes to the Base Station (BS). In the networks with densely deployed nodes, the observations are likely to be highly correlated in the space domain. This type of correlation is referred as spatial correlation, which produces unfavorable redundant readings causing energy wasting. In this thesis, the main task is to reduce these nodes that have redundant readings by using a clustering algorithm called Event Based Clustering (EDC) algorithm. The clustering algorithm is based on exploiting the spatial correlation that is used to cluster the sensor nodes. Exploiting spatial correlation is proposed by using Vector Quantization (VQ) with respect to the distortion constraints. Furthermore, this algorithm is applied for energy harvesting sensor nodes. Also, the inessential sensor nodes that have correlated readings are reduced for improving the Energy-Efficacy (EE) with acceptable level of event signal reconstruction distortion at the sink node. After applying the EDC algorithm, the communication model is changed from single-hop model to two-hop (clustered-network) model. Hence, a theoretical framework of distortion function, i.e., accuracy level, for both single-hop and two-hop communication models is derived. Then, single-hop and two-hop communication models are compared in terms of achieved distortion level, number of alive nodes, and energy consumption for different sizes of event area. Finally, the effects of various harvested energy level on the clustered-network is studied with respect to the same terms.