Hiroshi Ohtera

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.

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. >

Software reliability growth models for testing-effort control

Abstract This paper discusses software reliability growth models incorporating the time-dependent behaviour of testing-effort expenditures. The testing effort measured by the testing resources such as CPU time, man-power, and executed test cases spent during software testing phase is described by Rayleigh and exponential curves. Using the software reliability growth models, a testing-effort control problem is considered to predict the amount of required testing-effort expenditures to achieve the objective number of software errors detected by the testing.

Optimum software-release time considering an error-detection phenomenon during operation

An optimal software-release problem of determining the time to stop testing and release the software system to the user is discussed. The optimality considers software error detection during not only the testing phase but also the operation phase. Two evaluation criteria for the problem are used: software reliability and mean time between failures. Numerical examples illustrate the optimal software-release problem. >

A testing-effort dependent software reliability model and its application

Abstract We discuss a software reliability growth model with testing-effort based on a nonhomogeneous Poisson process and its application to a testing-effort control problem. The time-dependent behaviour of testing-effort expenditures which is incorporated into software reliability growth is expressed by a Weibull curve due to the flexibility in describing a number of testing-effort expenditure patterns. Using several sets of actual software error data, the model fitting and examples of a testing-effort control problem are illustrated.

Testing-domain dependent software reliability models

Abstract An implemented software system is tested to detect and correct software errors during the testing phase in software development. The behavior of software error detection phenomenon in the testing phase can be described by a stochastic software reliability growth model. Software reliability is closely related to the quality and quantity of test-cases executed by software testing. Then, we discuss software reliability growth models based on testing-domain in a software system which is to cause the test-cases executed by testing. The models are formulated by nonhomogeneous Poisson processes. Further, we propose three kinds of different testing-domain functions. Finally, numerical illustrations of software reliability analysis for actual error data and comparison among existing models in terms of goodness-of-fit are presented.

Software reliability analysis based on a nonhomogeneous error detection rate model

Abstract A software reliability growth model discussed here assumes a nonhomogeneous error detection rate per error which characterizes a software reliability growth process during the testing phase. The methods for software reliability assessment are reviewed, and the model fitting and software reliability analysis for some software error data sets are presented. The optimum software release times are also given for the data analysis results.

Software reliability growth model for testing-domain

To evaluate the reliability of software quantitatively, a test is executed at the final stage of software development. Then errors existing in the software are corrected. In the testing phase, the functions and modules actually tested in the software are related closely to the quantity and quality of the imposed test case, and increase with the progress of the testing. In this paper, the set of functions and modules affected by the test in the software is called testing-domain. A software reliability growth model is discussed, where the increase/expansion of the testing-domain is assumed to be related to the error detection of the software errors. A result is shown where the model is applied to the actual software error data. A comparison is made from the viewpoint of matching with other software reliability growth models.