Distillation with labelled transition systems

Hamilton, Geoffrey William; Jones, Neil D.

doi:10.1145/2103746.2103753

Cited by 18 publications

(26 citation statements)

References 20 publications

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“…The predicates produced in our approach are simplified using the distillation transformation [13,15]. Distillation is a fold/unfold program transformation that builds on top of positive supercompilation [29], Figure 5: Weakest Liberal Precondition but is more powerful, thus allowing more simplifications to be performed.…”

Section: Distillationmentioning

confidence: 99%

“…The homeomorphic embedding relation was derived from results by Higman [16] and Kruskal [21] and was defined within term rewriting systems [5] for detecting the possible divergence of the term rewriting process. Variants of this relation have been used to ensure termination within positive supercompilation [28], distillation [13,15], partial evaluation [23] and partial deduction [2,22].…”

Section: Embeddingmentioning

confidence: 99%

“…Similarly to the induction-iteration method, our technique involves working backward through the iterations of a loop and determining the assertions that are true before each iteration. We use the distillation program transformation [13,15] to transform assertions into a simplified form that facilitates the identification of similarities and differences between them. Commonalities between these assertions are identified, and they are generalised accordingly to give a putative loop invariant that can then be verified.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generating Loop Invariants for Program Verification by Transformation

Hamilton

2017

Electron. Proc. Theor. Comput. Sci.

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Loop invariants play a central role in the verification of imperative programs. However, finding these invariants is often a difficult and time-consuming task for the programmer. We have previously shown how program transformation can be used to facilitate the verification of functional programs, but the verification of imperative programs is more challenging due to the need to discover these loop invariants. In this paper, we describe a technique for automatically discovering loop invariants. Our approach is similar to the induction-iteration method, but avoids the potentially exponential blow-up in clauses that can result when using this and other methods. Our approach makes use of the distil- lation program transformation algorithm to transform clauses into a simplified form that facilitates the identification of similarities and differences between them and thus help discover invariants. We prove that our technique terminates, and demonstrate its successful application to example programs that have proven to be problematic using other approaches. We also characterise the situations where our technique fails to find an invariant, and show how this can be ameliorated to a certain extent.Comment: In Proceedings VPT 2017, arXiv:1708.0688

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Section: Distillationmentioning

confidence: 99%

Section: Embeddingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generating Loop Invariants for Program Verification by Transformation

Hamilton

2017

Electron. Proc. Theor. Comput. Sci.

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“…1. Reduces inefficient intermediate data structures in a given program using an existing transformation technique called distillation [11]. (Section 3)…”

Section: Introductionmentioning

confidence: 99%

“…: A given program may contain a number of inefficient intermediate data structures. In order to reduce them, we use an existing transformation technique called distillation.Distillation[11] is a technique that transforms a program to remove intermediate data structures and yields a distilled program. It is an unfold/fold-based transformation that makes use of well-known transformation steps -unfold, generalise and fold[25] -and can potentially provide super-linear speedups to programs.…”

mentioning

confidence: 99%

Program Transformation to Identify List-Based Parallel Skeletons

Kannan

Hamilton

2016

Electron. Proc. Theor. Comput. Sci.

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Algorithmic skeletons are used as building-blocks to ease the task of parallel programming by abstracting the details of parallel implementation from the developer. Most existing libraries provide implementations of skeletons that are defined over flat data types such as lists or arrays. However, skeleton-based parallel programming is still very challenging as it requires intricate analysis of the underlying algorithm and often uses inefficient intermediate data structures. Further, the algorithmic structure of a given program may not match those of list-based skeletons. In this paper, we present a method to automatically transform any given program to one that is defined over a list and is more likely to contain instances of list-based skeletons. This facilitates the parallel execution of a transformed program using existing implementations of list-based parallel skeletons. Further, by using an existing transformation called distillation in conjunction with our method, we produce transformed programs that contain fewer inefficient intermediate data structures.

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Counterexample-Guided Partial Bounding for Recursive Function Synthesis

Nicolet

Farzan

2021

Computer Aided Verification

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Quantifier bounding is a standard approach in inductive program synthesis in dealing with unbounded domains. In this paper, we propose one such bounding method for the synthesis of recursive functions over recursive input data types. The synthesis problem is specified by an input reference (recursive) function and a recursion skeleton. The goal is to synthesize a recursive function equivalent to the input function whose recursion strategy is specified by the recursion skeleton. In this context, we illustrate that it is possible to selectively bound a subset of the (recursively typed) parameters, each by a suitable bound. The choices are guided by counterexamples. The evaluation of our strategy on a broad set of benchmarks shows that it succeeds in efficiently synthesizing non-trivial recursive functions where standard across-the-board bounding would fail.

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Distillation with labelled transition systems

Cited by 18 publications

References 20 publications

Generating Loop Invariants for Program Verification by Transformation

Generating Loop Invariants for Program Verification by Transformation

Program Transformation to Identify List-Based Parallel Skeletons

Counterexample-Guided Partial Bounding for Recursive Function Synthesis

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