Estimating software robustness in relation to input validation vulnerabilities using Bayesian networks

Estimating software robustness in relation to input validation vulnerabilities using Bayesian... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Software Quality Journal Springer Journals

Estimating software robustness in relation to input validation vulnerabilities using Bayesian networks

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Software Engineering/Programming and Operating Systems; Programming Languages, Compilers, Interpreters; Data Structures, Cryptology and Information Theory; Operating Systems
ISSN
0963-9314
eISSN
1573-1367
D.O.I.
10.1007/s11219-017-9359-5
Publisher site
See Article on Publisher Site

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.

Journal

Software Quality JournalSpringer Journals

Published: Mar 28, 2017

References

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