WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Conference Object Enhancing Genomic Data Sharing With Blockchain-Enabled Dynamic Consent in Beacon V2(Springernature, 2024) Gürdal, Gültekin; Ayav, Tolga; Tuğlular, Tuğkan; Ayav, Tolga; Tuglular, Tugkan; Oktay, Yavuz; Karakulah, Gokhan; 01. Izmir Institute of Technology; 07. Library; 03. Faculty of Engineering; 03.04. Department of Computer EngineeringConference Object Enhancing genomic data sharing with blockchain-enabled dynamic consent in beacon V2(Springernature, 2024) Gürdal, Gültekin; Tuğlular, Tuğkan; Ayav, Tolga; Ayav, Tolga; Tuglular, Tugkan; Oktay, Yavuz; Karakulah, Gokhan; 07. Library; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringConference Object Citation - WoS: 1Citation - Scopus: 1A Metric for Measuring Test Input Generation Effectiveness of Test Generation Methods for Boolean Expressions(IEEE, 2021) Ufuktepe, Ekincan; Ayav, Tolga; Ayav, Tolga; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThe literature includes several methods to generate test inputs for Boolean expressions. The effectiveness of those methods needs to be analyzed by extensive comparisons. To this end, mutation analysis is often benefited by applying a distinctively selected set of mutants on each test generation method. Mutation analysis provides substantive information about the effectiveness of a test suite by indicating the percentage of killed mutants, which is a common metric. However, as we claim and show in this paper, this metric alone is not sufficient to demonstrate the effectiveness of the methods. For a test generation method, the amount of generated test inputs is also an important attribute to evaluate effectiveness. To the best of our knowledge, there is no metric that measures the effectiveness within a scale taking into account several attributes. In this study, we propose a new metric to measure the effectiveness of test input generation methods, which takes into account both the number of killed mutants and the number of test inputs. We demonstrate our new metric on three well-known test input generation methods for Boolean expressions.