Shigeru Yamada

S-Shaped Reliability Growth Modeling for Software Error Detection

This paper investigates a stochastic model for a software error detection process in which the growth curve of the number of detected software errors for the observed data is S-shaped. The software error detection model is a nonhomogeneous Poisson process where the mean-value function has an S-shaped growth curve. The model is applied to actual software error data. Statistical inference on the unknown parameters is discussed. The model fits the observed data better than other models.

Software Reliability Growth Modeling: Models and Applications

This paper summarizes existing software reliability growth models (SRGMs) described by nonhomogeneous Poisson processes. The SRGMs are classified in terms of the software reliability growth index of the error detection rate per error. The maximum-likelihood estimations based on the SRGMs are discussed for software reliability data analysis and software reliability evaluation. Using actual software error data observed by software testing, application examples of the existing SRGMs are illustrated.

Software Reliability Growth Models with Testing-Effort

Many software reliability growth models have been proposed in the past decade. Those models tacitly assume that testing-effort expenditures are constant throughout software testing. This paper develops realistic software reliability growth models incorporating the effect of testing-effort. The software error detection phenomenon in software testing is modeled by a nonhomogeneous Poisson process. The software reliability assessment measures and the estimation methods of parameters are investigated. Testing-effort expenditures are described by exponential and Rayleigh curves. Least-squares estimators and maximum likelihood estimators are used for the reliability growth parameters. The software reliability data analyses use actual data. The software reliability growth models with testing-effort can consider the relationship between the software reliability growth and the effect of testing-effort. Thus, the proposed models will enable us to evaluate software reliability more realistically.

s-Shaped Software Reliability Growth Models and Their Applications

The s-shaped growth curves of detected software errors can be observed in software testing. The delayed s-shaped and inflection s-shaped software reliability growth models based on a nonhomogeneous Poisson process are discussed. The software reliability growth types of the models are investigated in terms of the error detection rate per error. In addition, a viable method for the software quality assessment, which integrates the capture-recapture method and the models above, is discussed, and its application to actual test data is illustrated.

Cost-Reliability Optimal Release Policies for Software Systems

This paper extends an optimal software release problem to both cost and reliability requirements. The optimum software release time is determined both by minimizing a total average software cost and satisfying a software reliability requirement. The underlying model is software reliability growth described by a nonhomogeneous Poisson process.

Optimal software release policies with simultaneous cost and reliability requirements

Abstract A cost-reliability optimal software release problem is investigated for three existing software reliability growth models. The decision policies on the optimum software release times are discussed by evaluating both software cost and software reliability criteria simultaneously.

Optimal allocation and control problems for software-testing resources

Two kinds of software-testing management problems are considered: testing-resource allocation to best use specified testing resources during module testing, and a testing-resource control problem concerning how to spend the allocated amount of testing-resource expenditures during it. A software reliability growth model based on a nonhomogeneous Poisson process is introduced. The model describes the time-dependent behavior of software errors detected and testing-resource expenditures spent during the testing. The optimal allocation and control of testing resources among software modules can improve reliability and shorten the testing stage. Based on the model, numerical examples of these two software testing management problems are presented. >

Optimum release policies for a software system with a scheduled software delivery time

Optimum release policies minimizing the total expected software cost with a scheduled software delivery time are discussed. Such cost considerations enable us to make a release decision as to when to transfer a software system from testing phase to operational phase. The underlying reliability model describing a software error occurrence phenomenon is a software reliability growth model based on a non-homogeneous Poisson process. It is assumed that the penalty cost functions due to delay for a scheduled software delivery time are proportional and exponential to time, respectively. We consider two cases; when the scheduled software delivery time is a constant and when it is a random variable with an arbitrary distribution. Four useful theorems are derived to determine optimum release times of a software system for operational use. Numerical examples are shown to illustrate the results.

Nonhomogeneous Error Detection Rate Models for Software Reliability Growth

Software reliability growth models with nonhomogeneous error detection rate are discussed. The models provide a plausible description in case that the software reliability growth is influenced by two types of errors: Types 1 and 2 errors. Type 1 (Type 2) errors are easy (difficult) to be detected. The software reliability growth models considered here are described by a nonhomogeneous Poisson process. First, the software reliability growth model which uses the testing time as the unit of error detection period Is discussed. The error detection rate per error for such an error detection process Is a time-dependent function. The meaningful assessment measures for software reliability are presented. The model parameters are estimated by a method of maximum likelihood. The asymptotic properties of the maximum likelihood estimates of the model parameters are derived and their application to the estimation of the assessment measures for software reliability is obtained. The optimum release policies for operational use of a software system based on the model are presented. Finally, the discrete software reliability growth modeling with two types of errors is discussed. The number of test runs instead of the testing time is used as the unit of the error detection period.

Optimal software release policies for a non-homogeneous software error detection rate model

Abstract Optimal software release policies, which decide when to stop testing a software system and transfer it to the user, are discussed for a software reliability growth model with nonhomogeneous error detection rate. Two evaluation criteria, i.e. total expected software cost and software reliability, are introduced. The optimal software release policies respectively derived for each criterion and those simultaneously evaluating both criteria are discussed. Numerical examples on those optimal software release policies are presented for illustration.