<b>Revolutionizing Database Design through a Scalable, Formal, Learning-Powered</b><b>Systematic Tradeoff Analysis</b>

Optimizing database design is crucial for the performance of any software systems reliant on databases. However, current database design tools offer limited support for evaluating the performance implications of database designs. Instead, they often rely on single-point solutions guided by static heuristics, offering limited insights. While state-of-the-art design tradeoff analysis tools extend greater assistance, their usage becomes impractical when dealing with large-scale, real-world systems hosting thousands of potential designs. Specifically, the exorbitant cost associated with dynamically analyzing the performance of each conceivable design impedes the scalability of these cutting-edge techniques. In this paper, we introduce an innovative solution grounded in machine learning to efficiently and scalably identify and present optimal tradeoffs within the realm of database designs for object-oriented software systems. Our approach centers on training a transformer model using a dataset of formally analyzed database designs. This trained model is then employed to identify Pareto-optimal design tradeoffs, drastically reducing the number of design candidates requiring dynamic evaluation. These select designs can subsequently undergo evaluation and comparison, with the designs offering the best tradeoffs presented to the user, significantly reducing the time required compared to state-of-the-art techniques.The extensive experiments across various software database systems corroborate our approach's high effectiveness in identifying optimal design alternatives overlooked by leading analysis tools. Additionally, our results demonstrate an impressive 98.21 % improvement in analysis efficiency compared to the current state of the art, reducing tradeoff analysis time from 58 days to just 43 minutes—a remarkable advancement in efficiency.