A new technique for strictness analysis

Wright, David A.

doi:10.1007/3540539816_70

Cited by 26 publications

(28 citation statements)

References 21 publications

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“…The same remarks apply to e.g. [Wri91] and reflect a quite general phenomenon, cf. the claims made in [Wan93, p. 137]:…”

Section: Proving the Translation Correctmentioning

confidence: 57%

“…Wright has proposed alternative type systems [Wri91] and [Wri92]. The idea is to annotate the arrows: if a function can be assigned type Int→ 0 Int this means that the function is strict, whereas a type Int→ 1 Int doesn't tell anything about strictness properties.…”

Section: Strictness Typesmentioning

confidence: 99%

“…This is also the approach taken in [Wri91]. In our framework (which in this respect differs from Wright's), we would like to find a "least type" (wrt.…”

Section: An Inference Algorithmmentioning

confidence: 99%

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Strictness Types: An Inference Algorithm and an Application

Amtoft¹

1993

DPB

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This report deals with strictness types, a way of recording whether a function needs its argument(s) or not. We shall present an inference system for assigning strictness types to expressions and subsequently we transform this system into an algorithm capable of annotating expressions with strictness types. We give an example of a transformation which can be optimized by means of these annotations, and finally we prove the correctness of the optimized transformation -at the same time proving the correctness of the annotation.Everything has been implemented; documentation can be found in appendix.

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“…The same remarks apply to e.g. [Wri91] and reflect a quite general phenomenon, cf. the claims made in [Wan93, p. 137]:…”

Section: Proving the Translation Correctmentioning

confidence: 57%

Section: Strictness Typesmentioning

confidence: 99%

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Strictness Types: An Inference Algorithm and an Application

Amtoft¹

1993

DPB

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“…The inference system is a restriction of Wright's strictness formalization [Wri91]. It can be seen to be essentially equivalent in expressive power to Baker-Finch's strictness analysis [BF92] when restricted to the language of the simply typed λ-calculus.…”

Section: Introductionmentioning

confidence: 99%

Iterative fixed point computation for type-based strictness analysis

Henglein

1994

Static Analysis

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Amtoft has formulated an "on-line" constraint normalization method for solving a strictness inference problem inspired by Wright. From the syntactic form of the normalized constraints he establishes that every program expression has a unique, most precise ("minimal") strictness judgement, given fixed values for the strictness annotation in negative position.We show that his on-line normalization is not required for proving his main syntactic result. Instead of normalizing the constraints during a bottom-up pass through the program we simply collect them first and solve them by standard iterative fixed point methods in a second phase. The main result follows from the fact that observable negative strictness variables only occur on the right-hand sides of the constraints. Furthermore, a standard iterative fixed point algorithm solves the constraints in linear time in the number of strictness variables in the constraint system whereas Amtoft's method requires exponential time.Our presentation is somewhat different than Amtoft's. We use a linearlogic inspired presentation of the programming language and the strictness inference system for it. This results in smaller constraint systems generated. A tight strictness inference normalization result shows that many inequational constraints can be replaced by equational ones. Thus the constraint system can be simplified and significantly reduced in size by efficient variable unification, which can be performed on-line during its construction. Finally, we give an asymptotic worst-case analysis of the size of constraint systems relative to a program with or without explicitly typed variable declarations.The main contributions in this paper are our analysis and method for solving type-based program analyses efficiently, not the design of new type-based program analyses. Generally, our method provides insight into the strengths of noncompositional program analysis algorithms; i.e., algorithms where the solution for a program is not computed as a function of * In Proc. 1st Int'l Static Analysis Symposium (SAS), Namur, Belgium, Sep. 1994, SpringerVerlag Lecture Notes in Computer Science, Vol. 864, pp. 395-407; also available as DIKU Semantics Report D-192 1 the corresponding solutions of its immediate components, such as in Algorithm W or in frontier-based abstract interpretation. 1 In particular, our method demonstrates the effective use of efficient iterative fixed point computation known from classical data flow analysis in type-based program analysis.

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“…This is inspired by the method of Wright [Wri91] -in [Wri92] he proves the correctness of his analysis (by means of a model for the λ-calculus), but does not consider any transformation based on the result of the analysis.…”

Section: Introductionmentioning

confidence: 99%

Minimal thunkification

Amtoft

1993

Static Analysis

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Abstract. By "thunkifying" the arguments to function applications and "dethunkifying" variables one can translate a λ-expression e into a λ-expression e , such that call-by-value evaluation of e gives the same result as call-by-name evaluation of e. By using the result of a strictness analysis, some of these thunkifications can be avoided. In this paper we present a type system for strictness analysis; present a translation algorithm which exploits the strictness proof tree; and give a combined proof of the correctness of the analysis/translation.

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A new technique for strictness analysis

Cited by 26 publications

References 21 publications

Strictness Types: An Inference Algorithm and an Application

Strictness Types: An Inference Algorithm and an Application

Iterative fixed point computation for type-based strictness analysis

Minimal thunkification

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