Abstract
Software reliability growth models are used to predict the quality of the software systems using statistical learning models. However,a large number of faults areremained undetected in small and medium applications. A large number of traditional reliability measures are used to test the software faults in theapplication development and testing process. But in real-time, new faults are included in the software testing and maintenance phases in order to find the reliability estimation. The main problem in the existing SRGMs include, difficult to handle large reliability data and these models are not applicable to statistical dependencies and independency measures. In the proposed model, a novel statistical dependency and independency-based quartile density distribution model is implemented to improve the reliability prediction rate. Experimental results proved that the present model has high reliability estimation probability than the traditional growth models in terms of skewness and peak-ness.
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