BÜHNOVÁ, Barbora, Stanislav CHREN and Lucie KREJČÍŘOVÁ. Failure Data Collection for Reliability Prediction Models: A Survey. Online. In Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures (QoSA'14). New York, NY, USA: ACM, 2014, p. 83-92. ISBN 978-1-4503-2576-9. Available from: https://dx.doi.org/10.1145/2602576.2602586.
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Basic information
Original name Failure Data Collection for Reliability Prediction Models: A Survey
Authors BÜHNOVÁ, Barbora (203 Czech Republic, guarantor, belonging to the institution), Stanislav CHREN (203 Czech Republic, belonging to the institution) and Lucie KREJČÍŘOVÁ (203 Czech Republic, belonging to the institution).
Edition New York, NY, USA, Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures (QoSA'14), p. 83-92, 10 pp. 2014.
Publisher ACM
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW ACM Portal
RIV identification code RIV/00216224:14330/14:00076155
Organization unit Faculty of Informatics
ISBN 978-1-4503-2576-9
Doi http://dx.doi.org/10.1145/2602576.2602586
Keywords in English Reliability prediction models; Failure parameters; Value estimation; Data collection; Survey
Tags International impact, Reviewed
Changed by Changed by: doc. Ing. RNDr. Barbora Bühnová, Ph.D., učo 39394. Changed: 21/11/2014 18:52.
Abstract
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.
Links
MUNI/A/0765/2013, interní kód MUName: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)
Investor: Masaryk University, Category A
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