Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7589
Title: Estimating software robustness in relation to input validation vulnerabilities using Bayesian networks
Authors: Ufuktepe, Ekincan
Tuğlular, Tuğkan
Keywords: Bayesian networks
Input validation vulnerabilities
Robustness (control systems)
Estimating software
Issue Date: Jun-2018
Publisher: Springer Verlag
Source: Ufuktepe, E., and Tuğlular, T. (2018). Estimating software robustness in relation to input validation vulnerabilities using Bayesian networks. Software Quality Journal, 26(2), 455-489. doi:10.1007/s11219-017-9359-5
Abstract: 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.
URI: https://doi.org/10.1007/s11219-017-9359-5
https://hdl.handle.net/11147/7589
ISSN: 0963-9314
0963-9314
1573-1367
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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