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Alan Mycroft

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
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4

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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FSCD Conference 2022 Conference Paper

Galois Connecting Call-by-Value and Call-by-Name

  • Dylan McDermott
  • Alan Mycroft

We establish a general framework for reasoning about the relationship between call-by-value and call-by-name. In languages with side-effects, call-by-value and call-by-name executions of programs often have different, but related, observable behaviours. For example, if a program might diverge but otherwise has no side-effects, then whenever it terminates under call-by-value, it terminates with the same result under call-by-name. We propose a technique for stating and proving these properties. The key ingredient is Levy’s call-by-push-value calculus, which we use as a framework for reasoning about evaluation orders. We construct maps between the call-by-value and call-by-name interpretations of types. We then identify properties of side-effects that imply these maps form a Galois connection. These properties hold for some side-effects (such as divergence), but not others (such as mutable state). This gives rise to a general reasoning principle that relates call-by-value and call-by-name. We apply the reasoning principle to example side-effects including divergence and nondeterminism.

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FSCD Conference 2020 Conference Paper

Data-Flow Analyses as Effects and Graded Monads

  • Andrej Ivaskovic
  • Alan Mycroft
  • Dominic Orchard

In static analysis, two frameworks have been studied extensively: monotone data-flow analysis and type-and-effect systems. Whilst both are seen as general analysis frameworks, their relationship has remained unclear. Here we show that monotone data-flow analyses can be encoded as effect systems in a uniform way, via algebras of transfer functions. This helps to answer questions about the most appropriate structure for general effect algebras, especially with regards capturing control-flow precisely. Via the perspective of capturing data-flow analyses, we show the recent suggestion of using effect quantales is not general enough as it excludes non-distributive analyses e. g. , constant propagation. By rephrasing the McCarthy transformation, we then model monotone data-flow effects via graded monads. This provides a model of data-flow analyses that can be used to reason about analysis correctness at the semantic level, and to embed data-flow analyses into type systems.

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