This paper discusses improvements to conventional software reliability analysis models by making the assumptions on which they are based more realistic. In an actual project environment, sometimes no more information is available than reliability data obtained from a test report. The models described here are designed to resolve the problems caused by this constraint on the availability of reli...

Several software reliability growth models (SRGMs) have been presented in the literature in the last three decades. These SRGMs take into account different testing environment depending on size and efficiency of testing team, type of components and faults, design of test cases, software architecture etc. The plethora of models makes the model selection an uphill task. Recently, some authors hav...

Till now there have been many Software Reliability models developed for assessing the reliability of software product. Most of these are based upon past failure data gathered during the testing phase. These models have been utilized to evaluate the quality of the software and for future predication of reliability. They have been used in many critical management decision making problems that occ...

The software reliability growth models (such as NHPP models) are frequently used in software reliability prediction. Estimation of parameters in these models is often done by point estimation. However, some numerical problems arise and make the actual computation hard, especially for automated reliability prediction tools. Here confidence interval computation in Goel-Okumoto model and S-shaped ...

This paper affirms that the quantification of life-critical software reliability is infeasible using statistical methods whether applied to standard software or fault-tolerant software. The classical methods of estimating reliability are shown to lead to exhorbitant amounts of testing when applied to life-critical software. Reliability growth models are examined and also shown to be incapable o...