Gyemin Lee

A new approach to an N/G/1 queue

We propose a new approach that gives a one-step computational algorithm to directly obtain the queue length distribution of an N/G/1 queueing system. The new approach is based on the supplementary variable method and the matrix–analytic method. We shall show that this approach enables us to derive the joint distribution of the queue length and the elapsed service time.

Analysis of the M/D/1-type queue based on an integer-valued first-order autoregressive process

In this paper, we propose a queueing model based on an integer-valued first-order autoregressive(INAR(1)) process. We derive the queue length distribution and its asymptotic decay rate of the proposed model. Also, our numerical study shows that the new model can be considered as an alternative approach to the well-known MMPP/D/1 queue in terms of performance and amount of computational work.

A Class of Discrete Time Coverage Growth Functions for Software Reliability Engineering

Coverage-based NHPP SRGMs have been introduced in order to incorporate the coverage growth behavior into the NHPP SRGMs. The coverage growth function representing the coverage growth behavior during testing is thus an essential factor of the coverage-based NHPP SRGMs. This paper proposes a class of discrete time coverage growth functions and illustrates its application to real data sets.

A General Coverage-Based NHPP SRGM Framework

This paper first discusses the existing non-homogeneous Poisson process(NHPP) software reliability growth model(SRGM) frameworks with respect to capability of representing software reliability growth phenomenon. As an enhancement of representational capability a new general coverage-based NHPP SRGM framework is developed. Issues associated with application of the new framework are then considered.

Selection of a Predictive Coverage Growth Function

A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

Virtual Coverage: A New Approach to Coverage-Based Software Reliability Engineering

It is common to measure multiple coverage metrics during software testing. Software reliability growth models and coverage growth functions have been applied to each coverage metric to evaluate software reliability; however, analysis results for the individual coverage metrics may conflict with each other. This paper proposes the virtual coverage metric of a normalized first principal component in order to avoid conflicting cases. The use of the virtual coverage metric causes a negligible loss of information.

Estimation of Coverage Growth Functions

A recent trend in software reliability engineering accounts for the coverage growth behavior during testing. The coverage growth function (representing the coverage growth behavior) has become an essential component of software reliability models. Application of a coverage growth function requires the estimation of the coverage growth function. This paper considers the problem of estimating the coverage growth function. The existing maximum likelihood method is reviewed and corrected. A method of minimizing the sum of squares of the standardized prediction error is proposed for situations where the maximum likelihood method is not applicable.