Improved Software Quality Assurance Techniques Using Safe Growth Model

In our lives are governed by large, complex systems with increasingly complex software, and the safety, security, and reliability of these systems has become a major concern. As the software in today’s systems grows larger, it has more defects, and these defects adversely affect the safety, security, and reliability of the systems. Software engineering is the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Software divides into two pieces: internal and external quality characteristics. External quality characteristics are those parts of a product that face its users, where internal quality characteristics are those that do not.Quality is conformance to product requirements and should be free. This research concerns the role of software Quality. Software reliability is an important facet of software quality. It is the probability of failure-free operation of a computer program in a specified environment for a specified time. In software reliability modeling, the parameters of the model are typically estimated from the test data of the corresponding component. However, the widely used point estimators are subject to random variations in the data, resulting in uncertainties in these estimated parameters. This research describes a new approach to the problem of software testing. The approach is based on Bayesian graphical models and presents formal mechanisms for the logical structuring of the software testing problem, the probabilistic and statistical treatment of the uncertainties to be addressed, the test design and analysis process, and the incorporation and implication of test results. Once constructed, the models produced are dynamic representations of the software testing problem. It explains need of the common test-and-fix software quality strategy is no longer adequate, and characterizes the properties of the quality strategy. KeywordsSoftware reliability, uncertainty analysis, Bayesian Approach, MEP, MC, TTF.