TY - GEN
T1 - A machine learning-based reliability assessment model for critical software systems
AU - Challagulla, Venkata U B
AU - Bastani, Farokh B.
AU - Paul, Raymond A.
AU - Tsai, Wei Tek
AU - Chen, Yinong
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Service-oriented architecture (SOA) techniques are being increasingly used for developing critical applications, especially network-centric systems. While the SOA paradigm provides flexibility and agility to better respond to changing business requirements, the task of assessing the reliability of SOA-based systems is challenging, especially for composite services. However, deriving high confidence reliability estimates for mission-critical systems can require huge costs and time. This paper presents a reliability assessment and prediction model for SOA-based systems. The services are assumed to be realized with reuse and logical composition of components. The model uses AI reasoning techniques on dynamically collected failure data of each service and its components as one of the evidences together with results from random testing. Memory-Based Reasoning technique and Bayesian Belief Networks are used as reasoning tools to guide the prediction analysis. The least tested and "high usage" input subdomains are identified and necessary remedial actions are taken depending on the predicted results from the proposed model. The model is illustrated using a simulated case study based on a real-time dataset from the NASA software repository.
AB - Service-oriented architecture (SOA) techniques are being increasingly used for developing critical applications, especially network-centric systems. While the SOA paradigm provides flexibility and agility to better respond to changing business requirements, the task of assessing the reliability of SOA-based systems is challenging, especially for composite services. However, deriving high confidence reliability estimates for mission-critical systems can require huge costs and time. This paper presents a reliability assessment and prediction model for SOA-based systems. The services are assumed to be realized with reuse and logical composition of components. The model uses AI reasoning techniques on dynamically collected failure data of each service and its components as one of the evidences together with results from random testing. Memory-Based Reasoning technique and Bayesian Belief Networks are used as reasoning tools to guide the prediction analysis. The least tested and "high usage" input subdomains are identified and necessary remedial actions are taken depending on the predicted results from the proposed model. The model is illustrated using a simulated case study based on a real-time dataset from the NASA software repository.
UR - http://www.scopus.com/inward/record.url?scp=37349109122&partnerID=8YFLogxK
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U2 - 10.1109/COMPSAC.2007.26
DO - 10.1109/COMPSAC.2007.26
M3 - Conference contribution
AN - SCOPUS:37349109122
SN - 9780769528700
T3 - Proceedings - International Computer Software and Applications Conference
SP - 79
EP - 86
BT - Proceedings - 31st Annual International Computer Software and Applications Conference, COMPSAC 2007
T2 - 31st Annual International Computer Software and Applications Conference, COMPSAC 2007
Y2 - 24 July 2007 through 27 July 2007
ER -