Principles of Program Analysis

Nielson, Flemming; Nielson, Hanne Riis; Hankin, Chris

doi:10.1007/978-3-662-03811-6

Cited by 1,160 publications

(1,010 citation statements)

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“…For efficiency reasons, we will utilize an independent attributes analysis [20], an analysis that does not maintain the correlation between different predicate values. Specifically, the set of concrete states arising at a program point will be abstracted by a value in P → {f alse, maybe}.…”

Section: Background: Distributive Predicate Abstractionsmentioning

confidence: 99%

Typestate Verification: Abstraction Techniques and Complexity Results

Field¹,

Goyal²,

Ramalingam³

et al. 2003

Static Analysis

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Abstract. We consider the problem of typestate verification for shallow programs; i.e., programs where pointers from program variables to heap-allocated objects are allowed, but where heap-allocated objects may not themselves contain pointers. We prove a number of results relating the complexity of verification to the nature of the finite state machine used to specify the property. Some properties are shown to be intractable, but others which appear to be quite similar admit polynomial-time verification algorithms. While there has been much progress on many aspects of automated program verification, we are not aware of any previous work relating the difficulty of typestate verification to properties of the finite state automaton. Our results serve to provide insight into the inherent complexity of important classes of verification problems. In addition, the program abstractions used for the polynomial-time verification algorithms may be of independent interest.

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Section: Background: Distributive Predicate Abstractionsmentioning

confidence: 99%

Typestate Verification: Abstraction Techniques and Complexity Results

Field¹,

Goyal²,

Ramalingam³

et al. 2003

Static Analysis

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“…Since in the implementation of JErlang, we perform isolated tests only for patterns, without taking into consideration the body of the join, the former would create multiple false unused variable warnings by the compiler. Therefore we perform a simplified (syntax) Reaching Definitions Analysis [9]. This allows us to create valid test functions for patterns: we leave the original name for the bound variables and substitute the unbound variables with the neutral as presented in listing 9.…”

Section: Methodsmentioning

confidence: 99%

“…To avoid unbound variable errors and execute the partial matching tests on non-linear patterns as soon as possible, we perform a variant of static Live Variable Analysis [9]. This enables us to determine whether the variable in the test for partial joins should be substituted with the neutral or left unchanged because it used more than once in the join.…”

Section: Methodsmentioning

confidence: 99%

JErlang: Erlang with Joins

Plociniczak¹,

Eisenbach²

2010

Lecture Notes in Computer Science

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Abstract.Erlang is an industrially successful functional language that uses the Actor model for concurrency. It supports the messagepassing paradigm by providing pattern-matching over received messages. Unfortunately coding synchronisation between multiple processes is not straightforward. To overcome this limitation we designed and implemented JErlang, a Join-Calculus inspired extension to Erlang. We provide a rich set of language features with our joins. We present implementation details of our two alternative solutions, a library and an altered VM. Our optimisations provide JErlang with good performance.

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“…A classical example is the Reaching Definitions analysis which determines, for each node in a flowchart, which definitions (assignments) reach it [19]. The results of this analysis might be used to perform a constant folding transformation of the program.…”

Section: Classical Abstract Interpretationmentioning

confidence: 99%

“…Often the properties that we are interested in are undecidable and so correctness is replaced by some approximation notion. We start by sketching the classical approach to semantics-based program analysis: abstract interpretation [4,19]. The semantics of a program f identifies some set V of values and specifies how the program transforms one value v 1 to another v 2 .…”

Section: Classical Abstract Interpretationmentioning

confidence: 99%

Probabilistic -calculus and Quantitative Program Analysis

Pierro

Hankin

Wiklicky

2005

Journal of Logic and Computation

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We show how the framework of probabilistic abstract interpretation can be applied to statically analyse a probabilistic version of the λ-calculus. The resulting analysis allows for a more speculative use of its outcomes based on the consideration of statistically defined quantities. After introducing a linear operator based semantics for our probabilistic λ-calculus Λp, and reviewing the framework of abstract interpretation and strictness analysis, we demonstrate our technique by constructing a probabilistic (first-order) strictness analysis for Λp.

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Principles of Program Analysis

Cited by 1,160 publications

References 0 publications

Typestate Verification: Abstraction Techniques and Complexity Results

Typestate Verification: Abstraction Techniques and Complexity Results

JErlang: Erlang with Joins

Probabilistic -calculus and Quantitative Program Analysis

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