Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Creation of Mutants by Using Centrality Criteria in Social Network Analysis(PeerJ Inc., 2020) Takan, SavaşMutation testing is a method widely used to evaluate the effectiveness of the test suite in hardware and software tests or to design new software tests. In mutation testing, the original model is systematically mutated using certain error assumptions. Mutation testing is based on well-defined mutation operators that imitate typical programming errors or which form highly successful test suites. The success of test suites is determined by the rate of killing mutants created through mutation operators. Because of the high number of mutants in mutation testing, the calculation cost increases in the testing of finite state machines (FSM). Under the assumption that each mutant is of equal value, random selection can be a practical method of mutant reduction. However, in this study, it was assumed that each mutant did not have an equal value. Starting from this point of view, a new mutant reduction method was proposed by using the centrality criteria in social network analysis. It was assumed that the central regions selected within this frame were the regions from where test cases pass the most. To evaluate the proposed method, besides the feature of detecting all failures related to the model, the widely-used W method was chosen. Random and proposed mutant reduction methods were compared with respect to their success by using test suites. As a result of the evaluations, it was discovered that mutants selected via the proposed reduction technique revealed a higher performance. Furthermore, it was observed that the proposed method reduced the cost of mutation testing.Article Citation - WoS: 2Privacy Issues in Post Dissemination on Facebook(Türkiye Klinikleri Journal of Medical Sciences, 2019) Sayın, Burcu; Şahin, Serap; Kogias, Dimitrios G.; Patrikakis, Charalampos Z.With social networks (SNs) being populated by a still increasing numbers of people who take advantage of the communication and collaboration capabilities that they offer, the probability of the exposure of people's personal moments to a wider than expected audience is also increasing. By studying the functionalities and characteristics that modern SNs offer, along with the people's habits and common behaviors in them, it is easy to understand that several privacy risks may exist, many of which people may be unaware of. In this paper, we focus on users' interactions with posts in a social network (SN), using Facebook as our research domain, and we emphasize some privacy leakages currently existing in Facebook's privacy policy. We also propose a solution to detected privacy issues, featuring a reference implementation of a tool based on a simulation, which visualizes the effect of potential privacy risks on Facebook and directs users to control their privacy. The proposed and simulated tool allows a post owner to observe the spreading area of his or her post depending on the selected privacy settings. Moreover, it provides preliminary feedback for all Facebook users that have interacted with this post, to make them aware of the possible privacy changes, aiming to give them a chance to protect the privacy of their interaction on this post by deleting it when an unwanted privacy change takes place. Finally, an online survey to increase privacy awareness in Facebook usage with over 500 volunteer participants has illuminated the need for such a tool or solution.Conference Object Citation - Scopus: 2A Comparative Study of Modularity-Based Community Detection Methods for Online Social Networks(CEUR Workshop Proceedings, 2018) Karataş, Arzum; Şahin, SerapDigital data represent our daily activities and tendencies. One of its main source is Online Social Networks (OSN) such as Facebook, YouTube etc. OSN are generating continuously high volume of data and define a dynamic virtual environment. This environment is mostly represented by graphs. Analysis of OSN data (i.e.,extracting any kind of relations and tendencies) defines valuable information for economic, socio-cultural and politic decisions. Community detection is important to analyze and understand underlying structure and tendencies of OSNs. When this information can be analysed successfully, software engineering tools and decision support systems can produce more successful results for end users. In this study, we present a survey of selected outstanding modularity-based static community detection algorithms and do comparative analysis among them in terms of modularity, running time and accuracy. We use different real-world OSN test beds selected from SNAP dataset collection such as Facebook Ego network, Facebook Pages network (Facebook gemsec), LiveJournal, Orkut and YouTube networks.