Type analysis of Prolog using type graphs

Hentenryck, Pascal Van; Cortesi, Agostino; Charlier, Baudouin Le

doi:10.1145/178243.178479

Cited by 28 publications

(32 citation statements)

References 10 publications

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“…A system for type analysis of Prolog is presented in [VCL93]. It takes a descriptive approach to typing, and the abstract domains are, in general, infinite.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Polymorphically Typed Logic Programs Using ACI-Unification

Smaus

2001

Logic for Programming, Artificial Intelligence, and Reasoning

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Analysis of (partial) groundness is an important application of abstract interpretation. There are several proposals for improving the precision of such an analysis by exploiting type information, icluding our own work with Hill and King [SHK00], where we had shown how the information present in the type declarations of a program can be used to characterise the degree of instantiation of a term in a precise and yet inherently finite way. This approach worked for polymorphically typed programs as in Gödel or HAL. Here, we recast this approach following Codish, Lagoon and Stuckey [CL00, LS01]. To formalise which properties of terms we want to characterise, we use labelling functions, which are functions that extract subterms from a term along certain paths. An abstract term collects the results of all labelling functions of a term. For the analysis, programs are executed on abstract terms instead of the concrete ones, and usual unification is replaced by unification modulo an equality theory which includes the well-known ACI-theory. Thus we generalise [CL00, LS01] w.r.t. the type systems considered and relate those two works.

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“…A system for type analysis of Prolog is presented in [VCL93]. It takes a descriptive approach to typing, and the abstract domains are, in general, infinite.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Polymorphically Typed Logic Programs Using ACI-Unification

Smaus

2001

Logic for Programming, Artificial Intelligence, and Reasoning

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“…We can capture this by the Boolean formula t 3 ↔ (t 1 ∧ t 2 ). 10 The reasoning is based on the following facts:…”

Section: Finite-tree Dependenciesmentioning

confidence: 99%

Finite-tree analysis for constraint logic-based languages

Bagnara

Gori

Hill

et al. 2004

Information and Computation

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Logic languages based on the theory of rational, possibly infinite, trees have much appeal in that rational trees allow for faster unification (due to the safe omission of the occurs-check) and increased expressivity (cyclic terms can provide very efficient representations of grammars and other useful objects). Unfortunately, the use of infinite rational trees has problems. For instance, many of the built-in and library predicates are ill-defined for such trees and need to be supplemented by run-time checks whose cost may be significant. Moreover, some widely used program analysis and manipulation techniques are correct only for those parts of programs working over finite trees. It is thus important to obtain, automatically, a knowledge of the program variables (the finite variables) that, at the program points of interest, will always be bound to finite terms. For these reasons, we propose here a new data-flow analysis, based on abstract interpretation, that captures such information. We present a parametric domain where a simple component for recording finite variables is coupled, in the style of the open product construction of Cortesi et al., with a generic domain (the parameter of the construction) providing sharing information. The sharing domain is abstractly specified so as to guarantee the correctness of the combined domain and the generality of the approach. This finite-tree analysis domain is further enhanced by coupling it with a domain of Boolean functions, called finite-tree dependencies, that precisely captures how the finiteness of some variables influences the finiteness of other variables. We also summarize our experimental results showing how finite-tree analysis, enhanced with finite-tree dependencies, is a practical means of obtaining precise finiteness information

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“…The widening of our previous type T ll with k=1 will yield the same result than the functor widening (Example 4.3), whereas with k=2 will yield the same result as restricted shortening (Example 4.7). Topological Clash Widening Van Hentenryck et al [11] proposed the first widening operator that takes into account two consecutive approximations to the type being inferred. After merging the two -i.e., calculating their least upper bound, the result is compared with the previous approximation to try to "guess" where the type is growing.…”

Section: Wideningsmentioning

confidence: 99%

More Precise Yet Efficient Type Inference for Logic Programs

Vaucheret

Bueno

2002

Static Analysis

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Type analyses of logic programs which aim at inferring the types of the program being analyzed are presented in a unified abstract interpretation-based framework. This covers most classical abstract interpretation-based type analyzers for logic programs, built on either top-down or bottom-up interpretation of the program. In this setting, we discuss the widening operator, arguably a crucial one. We present a new widening which is more precise than those previously proposed. Practical results with our analysis domain are also presented, showing that it also allows for efficient analysis.

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Type analysis of Prolog using type graphs

Cited by 28 publications

References 10 publications

Analysis of Polymorphically Typed Logic Programs Using ACI-Unification

Analysis of Polymorphically Typed Logic Programs Using ACI-Unification

Finite-tree analysis for constraint logic-based languages

More Precise Yet Efficient Type Inference for Logic Programs

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