Proving correctness for SQL implementations of OCL constraints
Hoàng Nguyen Phuoc Bao, Manuel Clavel
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
In the context of the model-driven development of data-centric applications, OCL constraints play a major role in adding precision to the source models (e.g., data models and security models). Several code-generators have been proposed to bridge the gap between source models with OCL constraints and their corresponding database implementations. However, the database queries produced by these code-generators are signi ...
ficantly less efficient -- from the point of view of execution-time performance -- than the implementations manually written by database experts. In this paper, we propose a different approach to bridge the gap between models with OCL constraints and their corresponding database implementations. In particular, we introduce a model-based methodology for proving the correctness of manually written SQL implementations of OCL constraints. This methodology is based on a novel mapping from a significant subset of the SQL language into many-sorted first-order logic. Moreover, by leveraging on an already existing mapping from the OCL language into many-sorted first-order logic, we can use SMT solvers to automatically prove the correctness of SQL implementations of OCL constraints. To illustrate and show the applicability of our approach, we include in the paper a number of non-trivial examples. Finally, we report on the status of a suite of tools supporting our approach.
Results & Benchmarks
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In the context of the model-driven development of data-centric applications, OCL constraints play a major role in adding precision to the source models (e.g., data models and security models).
Implementation Evidence Summary
prakhar1989/awesome-courses is the closest maintained adjacent implementation (Matches contextual method/domain keyword: computer science). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 69947 GitHub stars.
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Evidence disclosure
Evidence graph: 3 refs, 3 links.
Utility signals: depth 100/100, grounding 85/100, status high.
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Closest related implementations
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- prakhar1989/awesome-coursesAdjacentConfidence: MediumStars: 69,947
Matches contextual method/domain keyword: computer science
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Tasks
Correctness, Programming language, Computer science, Implementation, SQL, Computer Networks and Communications, Physical Sciences
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