Modular operational test plans for inferences on software reliability based on a Markov model

Abstract

This paper considers the problem of assessing the reliability of a software system that can be decomposed into a finite number of modules. It uses a Markovian model for the transfer of control between modules in order to develop the system reliability expression in terms of the module reliabilities. An operational test procedure is considered in which only the individual modules are tested and the system is considered acceptable if, and only if, no failures are observed. The minimum number of tests required of each module is determined such that the probability of accepting a system whose reliability falls below a specified value R 0 is less than a specified small fraction β. This sample size determination problem is formulated as a two-stage mathematical program and an algorithm is developed for solving this problem. Two examples from the literature are considered to demonstrate the procedure.

Architecture-Based Software Reliability Analysis: Overview and Limitations

“…During the design phase, reliability constrained cost minimization and cost constrained reliability maximization could be the two objectives [40]. During the testing phase, the objective of the optimization may be to determine the allocation of testing effort so that the desired reliability objective is achieved [61], [78]. During the operational phase, optimization may be used to explore alternative configurations and to determine an optimal allocation of components to various nodes in a distributed network to achieve the desired performance and reliability.…”

Method for assessing software reliability of the document management system using the RFID technology

“…Without compromising the deliberations herein, it is possible to assume that the discussed software has one input and one output module. In the literature, the graph with the provided values is called the control transfer graph of the software [1,16].…”

Weighted Markov chains and graphic state nodes for information retrieval

“…Zhang (2001), for example, applies Markov to artificial intelligence tasks in uncertain environments. Paterman (1990), Cassandra (1998), Rajgopal and Mazumdar (2002) evaluate statistical inference in systems. Few researchers focus specifically on information retrieval: Chen and Cooper (2002) studied stochastic modeling of use patterns.…”

“…No IR system is assured of complete relevancy (or convergence) in its initial state, each node contains irrelevant documents when user node i ) = (1− r i ). “The Markov chain thus has n + 2 states and a transition matrix Q , where q ij = r i p ij for i = 1, 2, …, n and j = 1, 2, …, n , S; q iF = 1 − r i for i = 1, 2, … n ; and q FF = q SS = 1” (Rajgopal and Mazumdar, 2002, 360).…”