Modeling the Role of Testing Coverage in the Software Reliability Assessment

To assure the reliability and quality of the final product, testing is an essential and crucial part in the software development cycle. During this process, fault correction/detection activities are carried out to increase the reliability of the software. The non-homogeneous Poisson Process (NHPP) is the basis of the investigated software reliability growth models (SRGMs), which are based on the supposition that the number of faults found is affected by the amount of code covered during testing and that the amount of code covered during testing depends on the testing effort expended. This research takes into consideration several testing coverage functions: exponential, delayed S-shaped and logistic distributions, to propose three SRGMs that are based on testing efforts. For testing effort expenditure Weibull distribution has been employed. Two real failure datasets have been utilised to validate the proposed models, and their performance is evaluated using four goodness-of-fit metrics, including predictive ratio risk (PRR), coefficient of determination (R^2 ), predictive power (PP) and mean square error (MSE). Sensitivity analysis of cost requirement-based release time of software for exponential function has been done by using a genetic algorithm, which minimized the overall cost of the software subject to the requirement for reliability.