Software Reliability Demonstration Testing Research Articles

Explore One Factor of Affecting Software Reliability Demonstration Testing Result

Explore One Factor of Affecting Software Reliability Demonstration Testing Result

Software Reliability Demonstration Testing Scheme of Prior Dynamic Integration Bayesian Method based on the Idea of Decreasing Function

Software Reliability Demonstration Testing Scheme of Prior Dynamic Integration Bayesian Method based on the Idea of Decreasing Function

Determining the Minimal Software Reliability Test Effort by Stratified Sampling

Software reliability testing provided the necessary guarantee for improving software reliability level and estimation. The traditional method for generating software reliability test cases could be seen as a simple random sampling, which was randomly choosen according to the operational profile defined by Musa. The stratified sampling was well known as a complex but more accurate sampling approach which could make the sampling results more accurate and less variance by dividing the population into several subsets and randomly sampling from each subset respectively. First, this paper introduced the traditional approach to determine the number of test cases in the discrete software reliability demonstration testing. Second, the background of the stratified sampling and necessary condition for the minimal test effort based on the stratified sampling were put forward. Third, the new method’s principle and details were analyzed and a sample was given to show the method’s effectiveness.Finally, the future work was discussed. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3041

Software Reliability Demonstration Testing Plan Based on Bayesian Theory

For highly reliable software, it is expensive, time consuming or even infeasible to perform reliability testing by traditional Bayesian-based software reliability demonstration testing plan. Meanwhile, when determining the prior distribution function, people only consider conjugate prior distribution in order to realize the calculation in mathematics but ignores the characteristics of highly reliable software. To address this problem, by introducing the construction method of multilayer prior distribution function into the software reliability demonstration testing plan, this paper put forward a new Bayesian software reliability demonstration testing plan with zero-failure data, which can be used for highly reliable software reliability demonstration testing. Finally through examples, it illustrated the superiority of the method.

Minimum Test Case for Discrete-Type Software Reliability Testing by Stratified Sampling

The traditional approach for generating software reliability test cases according to operational profile (OP) can be regarded as a simple random sampling. The stratified sampling is a complex but more accurate sampling approach which can make the sampling results more accurate and less variance by dividing the population into several subsets and randomly sampling from each subset respectively. Firstly, this paper introduced the traditional method which can be used to determine the number of test cases in the discrete-type software reliability demonstration testing. Secondly, the necessary condition for the minimum test case based on the stratified sampling was studied. Then a new approach was proposed and its principle was analyzed for determining the minimum test case of the discrete-type software by using the stratified sampling. Finally, the future work was discussed.

Continuous model for software reliability demonstration testing considering damage size of software failures

This study proposes a continuous model for software reliability demonstration testing, which takes the damage size of software failures into consideration. In the proposed procedure, the software being tested is accepted if the number of software failures does not exceed a prespecified value, s and if the total damage size of software failures is less than another prespecified value, d. Based on the producer's and consumer's risks, an algorithm for designing a software reliability demonstration test is discussed.

A comparison of methods for calculating the duration of software reliability demonstration testing, particularly for safety-critical systems

A comparison of methods for calculating the duration of software reliability demonstration testing, particularly for safety-critical systems

Software reliability demonstration testing with consideration of damage size of software failures

This article describes an extension of the conventional reliability demonstration testing method that is based only on the number of software failures. A reliability demonstration testing method that also considers the size of the damage by the failure is proposed. Both the software used continuously in time (continuous model) and the software used discretely in time (discrete model) are considered. For each model of reliability demonstration testing, the producer's risk and the consumer's risk are formulated. A method is shown by which the testing can be designed when those risks are specified. Solution procedures are shown for the case where the damage size follows an exponential distribution and for the case where it follows a Poisson distribution. The characteristics of the proposed method are discussed using numerical examples. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(5): 10–21, 1999

Software reliability demonstration testing with consideration of damage size of software failures

Software reliability demonstration testing with consideration of damage size of software failures

A SUMMARY OF SOFTWARE RELIABILITY DEMONSTRATION TESTING MODELS

This paper summarizes models for software reliability demonstration testing (SRDT). The models are briefly classified into three types: (1) continuous models, (2) discrete models and (3) models considering damage sizes of software failures. Under the continuous models, the software product of interest is tested for time t and is accepted if the number of software failures in the test does not exceed a prespecified integer s. The values of design variables, t and s are determined based on (i) the concept of a statistical test (a statistical model) and (ii) the Kullback–Leibler information (a K–L model). The K–L model has less parameters to be prespecified than the statistical model. Under the discrete models for SRDT, the software of interest is tested with n input data sets and is accepted if the number of input data sets causing software failures in the test does not exceed a prespecified integer c. A statistical model as well as a K–L model is described for the discrete models. Neither continuous nor discrete models in the above take the damage size of software failures into consideration. The third type of models are continuous and discrete models which consider the cumulative damage size caused by software failures as well as the number of software failures in the test.