Constraint-based type inference and parametric polymorphism

Agesen, Ole

doi:10.1007/3-540-58485-4_34

Cited by 47 publications

(24 citation statements)

References 19 publications

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“…Let P and Q be datalog programs that do not share variable names 1 . The unfolding of P with respect to Q is a program de ned b y ((B 1 , .…”

Section: Unfolding and Minimizationmentioning

confidence: 99%

“…Hence, minimisation is an exact modular resolution method. 1 To lift this restriction, a renaming is needed.…”

Section: W) R(x Z W) S(u W) S(u V) S(y W) S(y V) S(y Z)}mentioning

confidence: 99%

“…One of the most important analyses for object-oriented languages is class analysis that computes an (over-)approximation of the set of classes that an expression can evaluate to at run-time 1,3,12,23,24]. Class analysis forms the foundation for static type checking for OO programs aimed at guaranteeing that methods are only invoked on objects that implement s u c h a method.…”

Section: Introductionmentioning

confidence: 99%

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Modular Class Analysis with DATALOG

Besson¹,

Jensen²

2003

Static Analysis

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datalog can be used to specify a variety of class analyses for object-oriented programs as variations of a common framework. In this framework, the result of analyzing a class is a set of datalog clauses whose least xpoint is the information analysed for. Modular class analysis of program fragments is then expressed as the resolution of open datalog programs. We provide a theory for the partial resolution of sets of open clauses and de ne a number of operators for reducing such open clauses.

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“…Let P and Q be datalog programs that do not share variable names 1 . The unfolding of P with respect to Q is a program de ned b y ((B 1 , .…”

Section: Unfolding and Minimizationmentioning

confidence: 99%

“…Hence, minimisation is an exact modular resolution method. 1 To lift this restriction, a renaming is needed.…”

Section: W) R(x Z W) S(u W) S(u V) S(y W) S(y V) S(y Z)}mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modular Class Analysis with DATALOG

Besson¹,

Jensen²

2003

Static Analysis

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“…Static analysis of object-oriented programs has attracted many researchers in recent years because mainly of the large potential for optimization related to dynamic dispatch (see e.g., [1,3,5]). The main difference between our approach and most existing work is that we propose a generic framework (for Java program analysis) explicitly based on abstract interpretation.…”

Section: Static Analysis Of Object-oriented Programsmentioning

confidence: 99%

“…They can be specialized by a suitable choice of these abstract descriptions to yield specific analysis. They basically integrate type analysis [1,5] and shape analysis [6,11,12,14,25].…”

Section: Introductionmentioning

confidence: 99%

Distinctness and Sharing Domains for Static Analysis of Java Programs

Pollet

Charlier

Cortesi

2001

ECOOP 2001 — Object-Oriented Programming

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Abstract. The application field of static analysis techniques for objectoriented programming is getting broader, ranging from compiler optimizations to security issues. This leads to the need of methodologies that support reusability not only at the code level but also at higher (semantic) levels, in order to minimize the effort of proving correctness of the analyses. Abstract interpretation may be the most appropriate approach in that respect. This paper is a contribution towards the design of a general framework for abstract interpretation of Java programs. We introduce two generic abstract domains that express type, structural, and sharing information about dynamically created objects. These generic domains can be instantiated to get specific analyses either for optimization or verification issues. The semantics of the domains are precisely defined by means of concretization functions based on mappings between concrete and abstract locations. The main abstract operations, i.e., upper bound and assignment, are discussed. An application of the domains to source-to-source program specialization is sketched to illustrate the effectiveness of the analysis.

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Dynamic versus Static Optimization Techniques for Object‐Oriented Languages

Hölzle¹,

Agesen

1995

Theory & Prac Object Systems

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Object‐oriented programs can be optimized either dynamically, i.e., based on run‐time information, or statically, i.e., based on program analysis alone. Two promising optimization techniques for object‐oriented languages are type feedback (dynamic) and concrete type inference (static). We directly compare the two techniques, evaluating their effectiveness on a suite of 23 Self programs while keeping other factors constant. Our results show that both systems inline >95% of all sends and deliver similar overall performance with one exception: Self's automatic coercion of machine integers to arbitrary‐precision integers upon overflow confounds type inference and slows down arithmetic‐intensive benchmarks. We also show that a system combining the two optimizations can combine their strengths and outperform each individual optimization. We discuss several other issues which, given the comparable run‐time performance, may influence the choice between type feedback and type inference.

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Constraint-based type inference and parametric polymorphism

Cited by 47 publications

References 19 publications

Modular Class Analysis with DATALOG

Modular Class Analysis with DATALOG

Distinctness and Sharing Domains for Static Analysis of Java Programs

Dynamic versus Static Optimization Techniques for Object‐Oriented Languages

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