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Most of the existing software reliability models assume time between failures to follow an exponential distribution. Develops a reliability growth model based on non‐homogeneous Poisson process with intensity function given by the power law, to predict the reliability of a software. Several authors have suggested the use of the non‐homogeneous Poisson process to assess the reliability growth of software and to predict their failure behaviour. Inference procedures considered by these authors have been Bayesian in nature. Uses an unbiased estimate of the failure rate for prediction. Compares the performance of this model with Bayes empirical‐Bayes models and a time series model. The model developed is realistic, easy to use, and gives a better prediction of reliability of a software.

Develops an optimization model to optimize software quality within budget and time limitations. Software quality is described through quality factors. This model assumes a non‐linear relationship between quality factors and the effort spent in the development phases. Enables a software manager to determine optimal levels of resource allocation to each of the phases of software development process to maximize the software quality subject to budget for time constraints. An illustrative example explains the application of this model. Also discusses sensitivity analysis of the model.

Considers an extension of the non‐homogeneous Poisson process to describe the software failure process more appropriately. This extension introduces a new structure for the intensity function of the non‐homogeneous Poisson process. Using this intensity function, develops a mathematical model to determine the optimal allocation of resources to be spent during the development to maximize the reliability within a budget.