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Cristina David

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2 papers

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AAAI Conference 2025 Conference Paper

Enhancing SQL Query Generation with Neurosymbolic Reasoning

Henrijs Princis
Cristina David
Alan Mycroft

We propose a neurosymbolic architecture aimed at boosting the performance of any Language Model (LM) for SQL query generation. This approach leverages symbolic reasoning to guide the LM's exploration of the search space by considering multiple paths, symbolically evaluating choices at each decision point to choose the next step, with the added novel ability to backtrack. A key innovation is the use of symbolic checks on both partially and fully generated SQL queries, enabling early truncation of unsuccessful search paths. Input consists of textual requirements on the desired query, along with optional example tuples to be selected by the query. Experiments on Xander, our open-source implementation, show it both reduces runtime and increases accuracy of the generated SQL. A specific result is an LM using Xander outperforming a four-times-larger LM.

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LPAR Conference 2015 Conference Paper

Using Program Synthesis for Program Analysis

Cristina David
Daniel Kroening
Matt Lewis

Abstract In this paper, we propose a unified framework for designing static analysers based on program synthesis. For this purpose, we identify a fragment of second-order logic with restricted quantification that is expressive enough to capture numerous static analysis problems (e. g. safety proving, bug finding, termination and non-termination proving, superoptimisation). We call this fragment the synthesis fragment. We build a decision procedure for the synthesis fragment over finite domains in the form of a program synthesiser. Given our initial motivation to solve static analysis problems, this synthesiser is specialised for such analyses. Our experimental results show that, on benchmarks capturing static analysis problems, our program synthesiser compares positively with other general purpose synthesisers.

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