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 Test Case Generation From Cause Effect Graphs(Izmir Institute of Technology, 2016) Kavzak Ufuktepe, Deniz; Ayav, Tolga; Ayav, TolgaCause-effect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any sufficiently comprehensive studies to generate test cases from these graphs. Yet there are several methods introduced to generate test cases from Boolean expressions. This thesis proposes to convert cause-effect graphs into Boolean expressions and find out the test sets using test input generation techniques for Boolean expressions, such as MI, MAX-A, CUTPNFP, MUMCUT, Unique MC/DC and Masking MC/DC. Generated test sets are compared by using mutation analysis according to their fault detection capabilities. Myers’ original test generation technique is also implemented and included in the mutation analysis. A tool is created which allows to generate test cases by using the implemented algorithms. The tool gets a “.graphml” file representing a cause- effect graph as an input and gives the generated test set as an output. In addition, mutation analysis can be done with the implemented tool. 14 Requirements of TCAS-II are used as an experiment. Results of the mutation testing for these requirements showed that MUMCUT technique has the highest mutant detection success for all fault types. Moreover, Unique MC/DC technique has detected highest number of mutants per test case.