City Research Online

Estimating worst case failure dependency with partial knowledge of the difficulty function

Bishop, P. G. and Strigini, L. (2014). Estimating worst case failure dependency with partial knowledge of the difficulty function. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8666, pp. 186-201. doi: 10.1007/978-3-319-10506-2_13

Abstract

For systems using software diversity, well-established theories show that the expected probability of failure on demand (pfd) for two diverse program versions failing together will generally differ from what it would be if they failed independently. This is explained in terms of a “difficulty function” that varies between demands on the system. This theory gives insight, but no specific prediction unless we have some means to quantify the difficulty function. This paper presents a theory leading to a worst case measure of “average failure dependency” between diverse software, given only partial knowledge of the difficulty function. It also discusses the possibility of estimating the model parameters, with one approach based on an empirical analysis of previous systems implemented as logic networks, to support pre-development estimates of expected gain from diversity. The approach is illustrated using a realistic safety system example.

Publication Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10506-2_13
Publisher Keywords: Safety, software reliability, fault tolerance, failure dependency, software diversity, difficulty function
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/4162
[img]
Preview
PDF - Accepted Version
Download (135kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login