Probabilistic models are crucial in the quantification of non-functional attributes in safety-and mission-critical software systems. These models are often re-evaluated in assessing the design decisions. Evaluation of such models is computationally expensive and exhibits exponential complexity with the problem size. This research aims at constructing an incremental quality evaluation framework and delta evaluation scheme to address this issue. The proposed technique will provide a computational advantage for the probabilistic quality evaluations enabling their use in automated design space exploration by architecture optimization algorithms. The expected research outcomes are to be validated with a range of realistic architectures and case studies from automotive industry.