A parallel abstract interpreter for JavaScript

Dewey, Kyle; Kashyap, Vineeth; Hardekopf, Ben

doi:10.1109/cgo.2015.7054185

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…From past works, we analyze the jQuery tests found in Andreasen and Mùller [2014], the Prototype.js and Scriptaculous tests from Wei et al [2016], and the test suite (excluding 7 benchmarks that require additional modelling of Firefox add-ons) used by Kashyap et al [2014] and Dewey et al [2015] (referred to collectively as łJSAI testsž henceforth).…”

Section: Benchmarkmentioning

confidence: 99%

Static analysis with demand-driven value refinement

Stein

Nielsen

Chang

et al. 2019

Proc. ACM Program. Lang.

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Static analysis tools for JavaScript must strike a delicate balance, achieving the level of precision required by the most complex features of target programs without incurring prohibitively high analysis time. For example, reasoning about dynamic property accesses sometimes requires precise relational information connecting the object, the dynamically-computed property name, and the property value. Even a minor precision loss at such critical program locations can result in a proliferation of spurious dataflow that renders the analysis results useless. We present a technique by which a conventional non-relational static dataflow analysis can be combined soundly with a value refinement mechanism to increase precision on demand at critical locations. Crucially, our technique is able to incorporate relational information from the value refinement mechanism into the non-relational domain of the dataflow analysis. We demonstrate the feasibility of this approach by extending an existing JavaScript static analysis with a demand-driven value refinement mechanism that relies on backwards abstract interpretation. Our evaluation finds that precise analysis of widely used JavaScript utility libraries depends heavily on the precision at a small number of critical locations that can be identified heuristically, and that backwards abstract interpretation is an effective mechanism to provide that precision on demand. CCS Concepts: • Theory of computation → Program analysis.

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Section: Benchmarkmentioning

confidence: 99%

Static analysis with demand-driven value refinement

Stein

Nielsen

Chang

et al. 2019

Proc. ACM Program. Lang.

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“…The experimental evaluation found that the analysis does not scale beyond 4 processors. Dewey et al [2015] present a parallel static analysis for JavaScript by dividing the analysis into an embarrassingly parallel reachability computation on a state transition system, and a strategy for selectively merging states during that reachability computation.…”

Section: Related Workmentioning

confidence: 99%

Deterministic parallel fixpoint computation

Kim

Venet

Thakur

2019

Proc. ACM Program. Lang.

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interpretation is a general framework for expressing static program analyses. It reduces the problem of extracting properties of a program to computing an approximation of the least fixpoint of a system of equations. The de facto approach for computing this approximation uses a sequential algorithm based on weak topological order (WTO). This paper presents a deterministic parallel algorithm for fixpoint computation by introducing the notion of weak partial order (WPO). We present an algorithm for constructing a WPO in almost-linear time. Finally, we describe Pikos, our deterministic parallel abstract interpreter, which extends the sequential abstract interpreter IKOS. We evaluate the performance and scalability of Pikos on a suite of 1017 C programs. When using 4 cores, Pikos achieves an average speedup of 2.06x over IKOS, with a maximum speedup of 3.63x. When using 16 cores, Pikos achieves a maximum speedup of 10.97x.Program analysis is a widely adopted approach for automatically extracting properties of the dynamic behavior of programs [Balakrishnan et al. 2010; Ball et al. 2004;Brat and Venet 2005;Delmas and Souyris 2007;Jetley et al. 2008]. Program analyses are used, for instance, for program optimization, bug finding, and program verification. To be effective, a program analysis needs to be efficient, precise, and deterministic (the analysis always computes the same output for the same input program) [Bessey et al. 2010]. This paper aims to improve the efficiency of program analysis without sacrificing precision or determinism.Abstract interpretation [Cousot and Cousot 1977] is a general framework for expressing static program analyses. A typical use of abstract interpretation to determine program invariants involves:C1 An abstract domain A that captures relevant program properties. Abstract domains have been developed to perform, for instance, numerical analysis [Cousot and HalbwachsEach index i ∈ [1, n] corresponds to a control point of the program, the unknowns X i of the system correspond to the invariants to be computed for these control points, and each F i is a monotone operator incorporating the abstract transformers and control flow of the program. C3 Computing an approximation of the least fixpoint of Eq. 1. The exact least solution of the system can be computed using Kleene iteration starting from the least element of A n provided A is Noetherian. However, most interesting abstract domains require the use of widening to ensure termination, which may result in an over-approximation of the invariants of the program. A subsequent narrowing iteration tries to improve the post solution via a downward fixpoint iteration. In practice, abstract interpreters compute an approximation of the least fixpoint. In this paper, we use "fixpoint" to refer to such an approximation of the least fixpoint.The iteration strategy specifies the order in which the equations in Eq. 1 are applied during fixpoint computation and where widening is performed. For a given abstraction, the efficiency, precision, and determinism o...

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“…Albarghouthi at el., [13] investigated parallel C interprocedural analysis, where based on the reachability in the call-graph multiple method analyzed intraprocedurally in parallel. Dewey at el., [14] explores parallel analysis of JavaScript by partitioning the state space of the program into regions that can be computed in parallel and those that require synchronizations of the parallel computations, i.e., merging points of the analysis.…”

Section: Related Workmentioning

confidence: 99%

Structurally Defined Conditional Data-Flow Static Analysis

Sherman

Dwyer

2018

Lecture Notes in Computer Science

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Data flow analysis (DFA) is an important verification technique that computes the effect of data values propagating over program paths. While more precise than flow-insensitive analyses, such an analysis is time-consuming. This paper investigates the acceleration of DFA by structural decomposition of the underlying control flow graph. Specifically, we explore the cost and effectiveness of dividing program paths into subsets by partitioning path suffixes at conditional statements, applying a DFA on each subset, and then combining the resulting invariants. This yields a family of independent DFA problems that are solved in parallel and where the partial results of each problem represent safe program invariants.Empirical evaluations reveal that depending on the DFA type and its conditional implementation the invariants for a large fraction of program points can be computed in less time than traditional DFA. This work suggests a strategy for an "anytime DFA" algorithm: computing safe program invariants as the analysis proceeds.

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A parallel abstract interpreter for JavaScript

Cited by 7 publications

References 19 publications

Static analysis with demand-driven value refinement

Static analysis with demand-driven value refinement

Deterministic parallel fixpoint computation

Structurally Defined Conditional Data-Flow Static Analysis

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