Title :
Software reliability model selection
Author :
Knafl, George J. ; Sacks, Jerome
Author_Institution :
Dept. of Comput. Sci. & Inf. Syst., DePaul Univ., Chicago, IL, USA
Abstract :
Model selection based on a predictive performance measure is compared to model selection based on maximum likelihood. Both procedures exhibit unstable relative performance when predictive performance is measured over time periods that may contain overly small proportions of total failures. Both procedures may be stabilized by bounding this proportion away from zero. In that case, both procedures exhibit similar predictive performance relative to the other. This provides evidence that model selection based on the commonly used maximum likelihood approach may in fact have good predictive performance
Keywords :
software reliability; maximum likelihood; model selection; predictive performance measure; software reliability; total failures; unstable relative performance; Computer science; Information systems; Maximum likelihood estimation; Parameter estimation; Predictive models; Software engineering; Software measurement; Software reliability; Statistics; Time measurement;
Conference_Titel :
Computer Software and Applications Conference, 1991. COMPSAC '91., Proceedings of the Fifteenth Annual International
Conference_Location :
Tokyo
Print_ISBN :
0-8186-2152-4
DOI :
10.1109/CMPSAC.1991.170245