Alloy*: a general-purpose higher-order relational constraint solver

@article{Milicevic2019AlloyAG,
  title={Alloy*: a general-purpose higher-order relational constraint solver},
  author={Aleksandar Milicevic and Joseph P. Near and Eunsuk Kang and Daniel Jackson},
  journal={Formal Methods in System Design},
  year={2019},
  pages={1-32}
}
The last decade has seen a dramatic growth in the use of constraint solvers as a computational mechanism, not only for analysis of software, but also at runtime. Solvers are available for a variety of logics but are generally restricted to first-order formulas. Some tasks, however, most notably those involving synthesis, are inherently higher order; these are typically handled by embedding a first-order solver (such as a SAT or SMT solver) in a domain-specific algorithm. Using strategies… 
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Methods Citations
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Bordeaux: A Tool for Thinking Outside the Box
TLDR
A prototype extension of the Alloy Analyzer is developed, named Bordeaux, that can find examples that are near the border of what is permitted, and hence might be desirable and help the user understand underconstraint bugs in the model.
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Automated Model Repair for Alloy
TLDR
The first technique for repairing declarative models in Alloy - a first order relational logic with transitive closure is introduced, ARepair, which integrates ideas from mutation testing and program synthesis to provide an effective solution for repairing Alloy models.
Automated Test Generation and Mutation Testing for Alloy
We present two novel approaches for automated testing of models written in Alloy – a well-known declarative, first-order language that is supported by a fully automatic SAT-based analysis engine. The…
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Two representative strategies for achieving correct code from formal specifications are found: one is based on proposing refinements until a certain concrete design is achieved and then an almost direct mapping from mathematical elements to the source code of some programming language is made.
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TLDR
A program synthesizer that integrates genetic algorithm with model finder and argues that different distance metrics for a fitness function may reveal behavioral differences in the genetic algorithm is developed.
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TLDR
This chapter introduces some of the novel application areas for Software Defined Networks (SDN) and Network Function Virtualization (NFV), and proposes automated methods to create formal models that satisfy a given set of requirements for SDN and NFV.
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Special Issue on Syntax-Guided Synthesis Preface
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