A Composite Approach Extracting Software Quality Attributes Assessing Operational Profiles

In modern software systems, software reliability is a defining quality characteristic, particularly in on-demand and critical needs settings where usage trends are prone to the evolution of time. The traditional black box reliability models do not sufficiently capture these dynamics, especially in component-based and reusable software architectures. In a bid to overcome such limitations, the present study presents the Level-Wise Composite Software Reliability method based on Operational Profile Evaluation (LCSR-OPE). The suggested model combines package-level measurements of software, software operational profile modelling and probabilistic analysis to approximate and refine software dependability in real usage contexts. The construction of user profiles through structural and complexity metrics clustering is carried out, but the operational behavior is measured by probability density functions in order to concentrate on testing and fault detection. Moreover, defect identification and reliability testing are narrowed down with a machine-learning-based fault classification model. Experimental performance on NASA software fault data sets establishes that the suggested method achieves high levels of fault-detection and reliability estimation as compared to traditional reliability models. The findings verify the effectiveness of the use of operational profiles and the level-based composite analysis in the reliability engineering of software excellence.