Paper Info
Title
Bayesian Network Based Software Reliability Prediction by Dynamic Simulation
Abstract
In order to solve the problem faced by reliability prediction and analysis for large-scale complex software system, Bayesian Network based software reliability modeling method and task flow oriented software reliability simulation prediction method are proposed in this paper. Bayesian Network based reliability modeling can calculate the initial reliability for complex software system by structure learning and parameter learning from the software architecture and the possible history data, on the basis of which Monte Carlo simulation can be used to setup the reliability logical relationship between different tasks in software system to realize the dynamic reliability prediction. This proposed method can comprehensively utilize the priori information of software architecture, history data and software task flows to conduct the dynamic reliability prediction and find the reliability weaknesses at the same time. One Train network Control & Management System (TCMS) software is selected as the experiment application to verify its feasibility and validity.
Year
DOI
Venue
2013
10.1109/SERE.2013.24
SERE
Keywords
Field
DocType
belief networks,reliability logical relationship,history data,reliability weakness,software reliability,software reliability prediction,learning (artificial intelligence),monte carlo simulation,reliability analysis,reliability modeling,bayesian network based software reliability prediction,parameter learning,train network control & management system software,large-scale complex software system,bayesian network based software reliability modeling method,software reliability modeling method,complex software system,software architecture,tcms software,bayesian network,monte carlo methods,reliability simulation,structure learning,software task,dynamic reliability prediction,reliability prediction,dynamic simulation,initial reliability,learning artificial intelligence,predictive models,software systems
Computer science,Software system,Software reliability testing,Software,Bayesian network,Software architecture,Software quality,Software construction,Reliability engineering,Software sizing
Conference
ISSN
ISBN
Citations 
2378-3877
978-1-4799-0406-8
2
PageRank 
References 
Authors
0.36
9
3
Name
Order
Citations
PageRank
Shunkun Yang13112.25
Minyan Lu2265.59
Lin Ge320.70