Software reliability is one of the most important characteristics of software quality. Its measurement and management technologies employed during software life cycle are essential for producing and maintaining quality/reliable software systems. It can also be monitored efficiently using Statistical Process Control (SPC). It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of Interval domain data using mean value function of Pareto type II distribution, which is based on Non-Homogenous Poisson Process (NHPP). The maximum likelihood estimation approach is used to estimate the unknown parameters of the model. We also present an analysis of failure data sets at a particular point.
Computer software has progressively turned out to be an essential component in modern technologies. Penalty costs resulting from software failures are often more considerable than software developing costs.Debugging decreases the error content but expands the software development costs. To improve the software quality, software reliability engineering plays an important role in many aspects throughout the software life cycle. In this paper, we incorporate both imperfect debugging and change-point problem into the software reliability growth model(SRGM) based on the well-known exponential distribution the parameter estimation is studied and the proposed model is compared with the some existing models in the literature and is find to be better.
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn, which may need more time. But, Sequential Analysis of Statistical science could be adopted in order to decide upon the reliability or unreliability of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). It is designed for continuous monitoring. The likelihood based SPRT proposed by Wald is very general and it can be used for many different probability distributions. In the present paper we propose the performance of SPRT on 6 data sets of Time domain data using Rayleigh model and analyzed the results. The parameters are estimated using Modified Genetic Algorithm.
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