.png){kind=icon}
.png){kind=icon}
Return
Bayesian network based software reliability prediction with an operational profile
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2004.11.034.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper uses a Bayesian network to model software reliability prediction with an operational profile. Due to the complexity of software products and development processes, software reliability models need to possess the ability to deal with multiple parameters. A Bayesian network exhibits a strong ability to adapt to problems involving complex variant factors. A special kind of Bayesian network named a Markov Bayesian network has been applied successfully into modeling software reliability prediction. However, the existing research did not pay enough attention to the fact that the failure characteristics of many software systems often depend on the specific operation performed. In this paper, an extended Markov Bayesian network is developed to model software reliability prediction with an operational profile. The extended Markov Bayesian network proposed in the paper is focused on discrete-time failure data. Methods to solve the network are proposed, and an example is used to illustrate the utilization of the model. (c) 2004 Elsevier Inc. All rights reserved.
Keywords:
software reliability
software failure rate
operational profile
Bayesian network
Markov Bayesian network
discrete-time failure data
software run
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
5.4K
Citations:
8.4K
Organization
No organization information available