Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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2014 - 20182

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Subject: search.filters.subject.Bayesian networks

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  • Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Estimating Software Robustness in Relation To Input Validation Vulnerabilities Using Bayesian Networks
    (Springer Verlag, 2018) Ufuktepe, Ekincan; Tuğlular, Tuğkan
    Estimating the robustness of software in the presence of invalid inputs has long been a challenging task owing to the fact that developers usually fail to take the necessary action to validate inputs during the design and implementation of software. We propose a method for estimating the robustness of software in relation to input validation vulnerabilities using Bayesian networks. The proposed method runs on all program functions and/or methods. It calculates a robustness value using information on the existence of input validation code in the functions and utilizing common weakness scores of known input validation vulnerabilities. In the case study, ten well-known software libraries implemented in the JavaScript language, which are chosen because of their increasing popularity among software developers, are evaluated. Using our method, software development teams can track changes made to software to deal with invalid inputs.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 1
    Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (university Level English Preparatory School Example)
    (Educational Consultancy and Research Center, 2014) Aslan, Burak Galip; Öztürk, Özlem; İnceoğlu, Mustafa Murat
    Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles Questionnaire. The questionnaire was adapted to Turkish for this experimental study conducted with respect to the visual/verbal and active/reflective dimensions of the model. A topic in EFL was chosen for the learning content design, which was also carried into the digital domain and remastered as separate learning scenes for different learning styles. Computer software was also implemented to carry out the experimental learning processes. A quasi-experimental pre-test, post-test design was conducted with 46 volunteers, with 23 students assigned each to a control and an experimental group to compare academic achievement between student-based learning and conventional computer-based learning. No significant difference was found in academic achievement between the control and experimental groups after the experimental treatment. The diagnostic performance of the proposed student modeling system was also compared with performances from similar studies. This student modeling system had a successful prediction rate of 41% on the visual/verbal dimension and 54% on the active/reflective dimension, respectively.