Abstract Conjunctive Partial Deduction Using Regular Types and Its Application to Model Checking

Leuschel, Michaël; Grüner, Stefan

doi:10.1007/3-540-45607-4_6

Cited by 10 publications

(20 citation statements)

References 39 publications

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“…Abstract Partial Deduction: Several approaches have been proposed which extend PD by using abstract substitutions [12,6,15,13]. In essence, such approaches are very similar to the abstract partial deduction with call propagation shown in Algorithm 1.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…Though all those proposals identify the need of propagating success substitutions, they either fail to do so or propose means for propagating success information which are not fully integrated with the APD algorithm and, in our opinion, do not fit in as nicely as the use of and-or trees. Also, these proposals are either strongly coupled to a particular (downward closed) abstract domain, i.e., regular types, as in [6,15] or do not provide the exact description of operations on the abstract domain which are needed by the framework, other than general correctness criteria [12,13]. However, the latter allow conjunctive PD, which is not available in our framework.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…The relationship between abstract interpretation [3] and partial evaluation [11] has received considerable attention (see for example [5,7,2,17,10,12,21,24,6,15,4,23,13] and their references). In order to motivate and illustrate our proposal for an integration of abstract interpretation and partial evaluation, we use the running example of Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Abstract Interpretation with Specialized Definitions

Puebla¹,

Albert

Hermenegildo³

2006

Static Analysis

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Abstract. The relationship between abstract interpretation and partial deduction has received considerable attention and (partial) integrations have been proposed starting from both the partial deduction and abstract interpretation perspectives. In this work we present what we argue is the first fully described generic algorithm for efficient and precise integration of abstract interpretation and partial deduction. Taking as starting point state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation and (abstract) partial deduction, we present an algorithm which combines the best of both worlds. Key ingredients include the accurate success propagation inherent to abstract interpretation and the powerful program transformations achievable by partial deduction. In our algorithm, the calls which appear in the analysis graph are not analyzed w.r.t. the original definition of the procedure but w.r.t. specialized definitions of these procedures. Such specialized definitions are obtained by applying both unfolding and abstract executability. Our framework is parametric w.r.t. different control strategies and abstract domains. Different combinations of such parameters correspond to existing algorithms for program analysis and specialization. Simultaneously, our approach opens the door to the efficient computation of strictly more precise results than those achievable by each of the individual techniques. The algorithm is now one of the key components of the CiaoPP analysis and specialization system.

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Section: Discussion and Related Workmentioning

confidence: 99%

Section: Discussion and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Abstract Interpretation with Specialized Definitions

Puebla¹,

Albert

Hermenegildo³

2006

Static Analysis

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show abstract

“…Abstract specialization can be seen as a semantic approach much in the same way as existing frameworks for partial deduction [35,26,10,28] and also as other attempts at the integration of partial evaluation and abstract interpretation of logic programs [29,15,30]. One of the main differences between abstract specialization and the aforementioned techniques is the underlying semantics.…”

Section: Discussionmentioning

confidence: 99%

“…Independently of our work in CiaoPP, recently there has been a lot of interest in the application of regular types for improving partial evaluation [15,30]. The use of non-deterministic regular types [16] presents an interesting trade-off since on one hand they allow improved accuracy but on the other they require a higher computational cost and their applicability to program specialization should be further explored.…”

Section: Abstract Domains and Widenings For Partial Evaluationmentioning

confidence: 99%

Abstract specialization and its applications

PueblaGermán

HermenegildoManuel

2003

SIGPLAN Not.

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The aim of program specialization is to optimize programs by exploiting certain knowledge about the context in which the program will execute. There exist many program manipulation techniques which allow specializing the program in different ways. Among them, one of the best known techniques is partial evaluation, often referred to simply as program specialization, which optimizes programs by specializing them for (partially) known input data. In this work we describe abstract specialization, a technique whose main features are: (1) specialization is performed with respect to "abstract" valúes rather than "concrete" ones, and (2) abstract interpretation rather than standard interpretation of the program is used in order to propágate information about execution states. The concept of abstract specialization is at the heart of the specialization system in CiaoPP, the Ciao system preprocessor. In this paper we present a unifying view of the different specialization techniques used in CiaoPP and discuss their potential applications by means of examples. The applications discussed include program parallelization, optimization of dynamic scheduling (concurreney), and integration of partial evaluation techniques.

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A Generic Framework for the Analysis and Specialization of Logic Programs

Puebla¹,

Albert

Hermenegildo³

2005

Logic Programming

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The relationship between abstract interpretation and partial deduction has received considerable attention and (partial) integrations have been proposed starting from both the partial deduction and abstract interpretation perspectives. In this work we present what we argue is the first fully described generic algorithm for efficient and precise integration of abstract interpretation and partial deduction. Taking as starting point state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation and (abstract) partial deduction, we present an algorithm which combines the best of both worlds. Key ingredients include the accurate success propagation inherent to abstract interpretation and the powerful program transformations achievable by partial deduction. In our algorithm, the calls which appear in the analysis graph are not analyzed w.r.t. the original definition of the procedure but w.r.t. specialized definitions of these procedures. Such specialized definitions are obtained by applying both unfolding and abstract executability. Our framework is parametric w.r.t. different control strategies and abstract domains. Different combinations of such parameters correspond to existing algorithms for program analysis and specialization. Simultaneously, our approach opens the door to the efficient computation of strictly more precise results than those achievable by each of the individual techniques. The algorithm is now one of the key components of the CiaoPP analysis and specialization system.

show abstract

Abstract Conjunctive Partial Deduction Using Regular Types and Its Application to Model Checking

Cited by 10 publications

References 39 publications

Abstract Interpretation with Specialized Definitions

Abstract Interpretation with Specialized Definitions

Abstract specialization and its applications

A Generic Framework for the Analysis and Specialization of Logic Programs

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