DocumentCode :
177170
Title :
An Approach to Modelling and Evaluating AGG Based Reliable Software Systems
Author :
Chen Li ; Linpeng Huang ; Luxi Chen ; Xiwen Wu
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2014
fDate :
1-3 Sept. 2014
Firstpage :
122
Lastpage :
125
Abstract :
AGG is a well established development environment which is used to model software systems in terms of graphs. Though AGG provides an appropriate basis for system modelling and verification, it can neither analyze nor evaluate the system reliability. In this paper, we propose an AGG-based strategy which, by combining generalized stochastic petri net (GSPN) and tools for reliability analysis, supports software system reliability modelling and evaluation. This work expands this idea in three directions: First, we give an AGG reliability model in which we add error attribute to AGG graph for capturing system error information, and at the same time performing the system error state transition through the AGG production. Second, we present how to map rules from the AGG reliability model into the GSPN one, which in turn can be used for reliability evaluation. The other direction is to use a Client/Server application to demonstrate our approach.
Keywords :
Petri nets; client-server systems; graph theory; program verification; software reliability; stochastic processes; AGG graph; AGG production; AGG reliability model; GSPN; client/server application; generalized stochastic Petri net; reliability evaluation; rule mapping; software system modeling; software system reliability modelling; system error information; system error state transition; system modelling; system verification; Analytical models; Portable computers; Production; Servers; Software reliability; Software systems; AGG; GSPN; Reliability; Software Modelling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Theoretical Aspects of Software Engineering Conference (TASE), 2014
Conference_Location :
Changsha
Type :
conf
DOI :
10.1109/TASE.2014.22
Filename :
6976577
Link To Document :
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