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@inproceedings{1195312,
author = {Bühnová, Barbora and Chren, Stanislav and Krejčířová, Lucie},
address = {New York, NY, USA},
booktitle = {Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures (QoSA'14)},
doi = {http://dx.doi.org/10.1145/2602576.2602586},
keywords = {Reliability prediction models; Failure parameters; Value estimation; Data collection; Survey},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York, NY, USA},
isbn = {978-1-4503-2576-9},
pages = {83-92},
publisher = {ACM},
title = {Failure Data Collection for Reliability Prediction Models: A Survey},
url = {http://dl.acm.org/citation.cfm?id=2602576.2602586&coll=DL&dl=ACM&CFID=406554470&CFTOKEN=52424171},
year = {2014}
}
TY - JOUR
ID - 1195312
AU - Bühnová, Barbora - Chren, Stanislav - Krejčířová, Lucie
PY - 2014
TI - Failure Data Collection for Reliability Prediction Models: A Survey
PB - ACM
CY - New York, NY, USA
SN - 9781450325769
KW - Reliability prediction models
KW - Failure parameters
KW - Value estimation
KW - Data collection
KW - Survey
UR - http://dl.acm.org/citation.cfm?id=2602576.2602586&coll=DL&dl=ACM&CFID=406554470&CFTOKEN=52424171
N2 - Design decisions made early in software development have great impact on the software product quality. Design-time reliability prediction is one of the techniques that support software engineers in early design decisions, based on the evaluation of reliability impact of the individual design alternatives. The accuracy of reliability prediction is critically dependent on the accuracy of reliability prediction models, which relies on uncertain failure parameters (such as the failure probability of component-internal actions). Although the effectiveness of the failure-parameter estimation critically influences the usability of the prediction techniques, the parameter estimation often relies on expert knowledge and is not receiving systematic attention. This paper aims to survey existing techniques for estimation and collection of failure parameters in architecture-based reliability prediction models, and presents the findings that can be learned from their detailed analysis.
ER -
BÜHNOVÁ, Barbora, Stanislav CHREN a Lucie KREJČÍŘOVÁ. Failure Data Collection for Reliability Prediction Models: A Survey. Online. In \textit{Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures (QoSA'14)}. New York, NY, USA: ACM, 2014, s.~83-92. ISBN~978-1-4503-2576-9. Dostupné z: https://dx.doi.org/10.1145/2602576.2602586.
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