Year: 2025
Type: article
Abstract: Optimizing object-relational database mapping (ORM) design is crucial for performance and scalability in modern software systems. However, widely used ORM tools offer limited support for exploring performance tradeoffs, often enforcing a single design and overlooking alternatives, which can lead to suboptimal outcomes. While systematic tradeoff analysis can reveal Pareto-optimal designs, its high computational cost and poor scalability hinder practical adoption. This paper presents DesignTradeoffSculptor, an extensible tool suite for efficient, scalable tradeoff analysis in ORM database design. Leveraging advanced Transformer-based deep learning models—trained and fine-tuned on formally analyzed database designs—and framing design exploration as a Natural Language Processing task, DesignTradeoffSculptor efficiently identifies and removes suboptimal designs, sharply reducing the number of candidates requiring costly tradeoff analysis. Experiments show that DesignTradeoffSculptor uncovers optimal designs missed by leading ORM tools and improves analysis efficiency by over 98.21%, reducing tradeoff analysis time from 15 days to just 18 minutes, demonstrating the transformative potential of integrating formal methods with deep learning.
DOI: 10.1145/3729344
Institution: University of Nebraska–Lincoln
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: SHF: Small: Efficient Formal Analysis of Evolving Software Systems, FMitF: Track I: Flow Modeling Meets Software Verification: Redesign Internet Congestion Control for Performance and Verifiability, CRII: SHF: Leveraging Synthesis for Dynamic Design Space Analysis, EAGER: CCF: SHF: Scalable Software Verification through Automated Derivation of Domain-Specific Optimization Tactics
OpenAlex ID: W4411449711