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On basis variables for efficient error detection
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Fairbrother, Jake and Leeke, Matthew (2018) On basis variables for efficient error detection. In: 15th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'17), Florida, USA, 6-10 Nov 2017. Published in: 2017 IEEE 15th International Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence & Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) ISBN 9781538619568. doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2017.82
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Official URL: http://doi.org/10.1109/DASC-PICom-DataCom-CyberSci...
Abstract
The development of dependable software invariably entails the design and location of error detection mechanisms. This software artefact type captures predicates over program variables in order to facilitate error detection. To ease the design of detectors, it is important to have (i) knowledge of the set of variables to be included in a predicate and (ii) an understanding of the structure of the predicate. In this paper, we address these problems by relating a previously defined software metric to the variables that feature in efficient error detection predicates. Specifically, based on fault injection analysis of three software systems, we show that error detection predicates based on the 25% most important variables in a software module provide a similar level efficiency to those predicates that are based on all variables and variables with high importance value appear at lower depths in the generated decision tree, thus implying that these variables provide the most information with regard to system failure and, hence, should be protected to provide proper software function. The implication of these results is that, in order to develop efficient error detection predicates, it is sufficient to only have knowledge of a basis set of important variables, simplifying the design of efficient detectors.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | Q Science > QA Mathematics > QA75 (Please use QA76 Electronic Computers. Computer Science) | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Computer software -- Development, Machine learning, Software engineering, Error-correcting codes (Information theory) | ||||||
| Journal or Publication Title: | 2017 IEEE 15th International Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence & Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781538619568 | ||||||
| Official Date: | 2 April 2018 | ||||||
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| DOI: | 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.82 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | 15th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'17) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Florida, USA | ||||||
| Date(s) of Event: | 6-10 Nov 2017 | ||||||
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