This work presents AutoSQL, a system that reconstructs SQL templates from Go ORM code that constructs a Code Index, a directed graph that captures structural dependencies between functions, types, and global variables as navigable edges and synthesizes SQL templates.
Abstract
Suboptimal SQL queries can significantly degrade the performance of cloud systems, motivating the extraction and auditing of SQL statements before deployment. However, Go ORM frameworks construct SQL imperatively through scattered method-call sequences, making it difficult to statically recover the resulting SQL templates. We present AutoSQL, a system that reconstructs SQL templates from Go ORM code. AutoSQL constructs a Code Index, a directed graph that captures structural dependencies between functions, types, and global variables as navigable edges. It then traces upstream call chains from ORM invocation sites to identify database-interacting functions as entry points. For each entry point, an LLM agent traverses the Code Index to collect code slices that influence SQL generation, switching to pattern-based search when the graph cannot resolve a retrieval goal. We call this strategy Hybrid Context Retrieval. Once sufficient context is collected, the agent synthesizes SQL templates. Evaluation on a benchmark of 579 test-covered entry points and 1,186 runtime-traced SQL statements from five large-scale Go repositories shows that AutoSQL achieves 68.04% to 72.18% recall, exceeding the static reachability baseline by 11.80% to 15.94% and outperforming existing methods by 8.52% to 21.50%.
DBcover is proposed, an LLM-driven database test generation framework that performs white-box, code-aware SQL test generation through contextual reasoning, and substantially outperforms existing fuzzers.
Yan-Kai Rong, Shuang Liu, Jin-Hao Dong et al.· Proceedings of the 2026 IEEE...· 0 citations
The Domain-Oriented Tooling Pattern is proposed: instead of generating SQL at query time, the model selects from a small set of domain-aligned tools whose parameterized queries encapsulate schema navigation, joins and business rules on the server side.
DAGSmith is introduced, to the best of the authors' knowledge the first holistic dependency-aware source-to-source rewriting system for SQL pipeline DAGs and enables dependency-edge simplification, non-local semantic reuse, downstream-aware pruning, pipeline-aware work placement, rewrite-materialization co-optimization, and frequency-aware optimization.
Results show that CODENS produces highly relevant and well-grounded answers, while qualitative feedback highlights a remaining challenge in concise, documentation-oriented synthesis.
Abdelhak Kelious, Chyrine Tahri, E. Bardet· Proceedings of the 2026 ACM...· 0 citations
Traditional query processing engines require continuous development and extensions to support new techniques and user requirements, and in some cases, entirely new systems must be built from scratch. However, these engines are difficult to extend due to their internal complexity, and building new systems demands significant engineering effort and cost. To address this, we demonstrate GenDB, a generative query engine that shifts query processing from manually engineered systems to query processing code generation driven by Large Language Models (LLMs). An early prototype of GenDB uses LLM agents to generate instance-optimized query execution code tailored to specific data, workloads, and hardware resources. This prototype suits offline code generation for repetitive, templated queries, since the upfront generation cost amortizes over many executions and correctness can be ensured through extensive fuzz testing and manual inspection. For ad-hoc queries, GenDB can work with a traditional DBMS in a hybrid architecture: the DBMS handles one-off queries, while GenDB speeds up frequent SQL templates. Our demonstration allows users to (1) visually and interactively explore how GenDB analyzes workloads, profiles hardware resources and underlying data, produces query plans, generates code based on them, and finally uses an optimizer to iteratively achieve a correct and efficient implementation; (2) use visual inspection and analysis to gain qualitative insights into why GenDB produces code that achieves significantly better performance than state-of-the-art query engines on two benchmarks: TPC-H and a newly constructed benchmark designed to reduce potential data leakage from LLM training data; and (3) upload their own data and queries to explore GenDB with different LLMs and query patterns.
Jiale Lao, Immanuel Trummer· arXiv.org· 0 citations
Datalog is a declarative query language that has proven highly effective for expressing static program analyses. Although Datalog has deep roots in database theory, most recent advances have largely emerged from the programming languages and compiler communities, with systems such as Souffl\'e. In contrast, modern relational engines have made significant progress in optimizing recursive SQL. This paper revisits the connection between Datalog and relational databases, advocating recursive SQL as a backend for Datalog evaluation. We present a compilation framework that translates Datalog programs, particularly those in the Linear Datalog fragment, into equivalent recursive SQL queries. To bridge the gap between Datalog and SQL, the compiler routes every program through an intermediate language called Midlog. The compiler additionally recovers functional dependencies from the program and exposes them as schema keys, unlocking the engine's standard query optimizations. This approach enables existing database engines to execute a broad class of program analyses, outperforming the Souffl\'e engine by up to an order of magnitude on the Umbra backend. Umbra achieves a geometric-mean speedup of 5.46$\times$ at 8 threads, whereas DuckDB is competitive with Souffl\'e single-threaded and is slower at 8 threads (geometric-mean speedup of 0.68$\times$). Furthermore, the generated SQL is portable; it runs on seven database systems without any engine modification. Our results highlight what the relational engines require to fully support Datalog for large-scale program analysis.
Unknown authors· 0 citations
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