TVAL+ : TVLA and Value Analyses Together

Ferrara, Pietro; Fuchs, Raphael; Juhasz, Uri

doi:10.1007/978-3-642-33826-7_5

Cited by 16 publications

(24 citation statements)

References 23 publications

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“…Our proposal enables a separation of concerns when designing static analyses that need to deal with complex data structures, as very different domains can be combined to abstract disjoint memory regions. A natural extension of our study would be to integrate other memory abstractions, as found in 3-valued logic shape analyses [19,1,12], into our framework.…”

Section: Resultsmentioning

confidence: 99%

An Abstract Domain Combinator for Separately Conjoining Memory Abstractions

2014

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Abstract. The breadth and depth of heap properties that can be inferred by the union of today's shape analyses is quite astounding. Yet, achieving scalability while supporting a wide range of complex data structures in a generic way remains a long-standing challenge. In this paper, we propose a way to side-step this issue by defining a generic abstract domain combinator for combining memory abstractions on disjoint regions. In essence, our abstract domain construction is to the separating conjunction in separation logic as the reduced product construction is to classical, non-separating conjunction. This approach eases the design of the analysis as memory abstract domains can be re-used by applying our separating conjunction domain combinator. And more importantly, this combinator enables an analysis designer to easily create a combined domain that applies computationally-expensive abstract domains only where it is required.

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Section: Resultsmentioning

confidence: 99%

An Abstract Domain Combinator for Separately Conjoining Memory Abstractions

2014

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“…TVLA [22] is the first and one of the most popular shape analysis engines. Ferrara et al [15] combined TVLA and value analyses in a generic way relying on substitutions. A further work [16] has plugged this combination in the framework we introduced in this paper.…”

Section: Tvla-based Shape Analysismentioning

confidence: 99%

“…When computing the TVLA semantics, the exit state may not be normalized. [15] then defines a normalization algorithm, and [16] proves that the substitutions it produces satisfy Propositions 1 and 2.…”

Section: Tvla-based Shape Analysismentioning

confidence: 99%

Generic Combination of Heap and Value Analyses in Abstract Interpretation

Ferrara

2014

Lecture Notes in Computer Science

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Abstract. Abstract interpretation has been widely applied to approximate data structures and (usually numerical) value information. One needs to combine them to effectively apply static analysis to real software. Nevertheless, they have been studied mainly as orthogonal problems so far. In this context, we introduce a generic framework that, given a heap and a value analysis, combines them, and we formally prove its soundness. The heap analysis approximates concrete locations with heap identifiers, that can be materialized or merged. Meanwhile, the value analysis tracks information both on variable and heap identifiers, taking into account when heap identifiers are merged or materialized. We show how existing pointer and shape analyses, as well as numerical domains, can be plugged in our framework. As far as we know, this is the first sound generic automatic framework combining heap and value analyses that allows to freely manage heap identifiers.

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“…Applying these techniques on our transformed programs, we expect them to infer the required information without any modification to their analyses. In order to track numerical values that are stored in fields, these approaches require further modifications [19].…”

Section: Related Workmentioning

confidence: 99%

Conditional termination of loops over heap-allocated data

Albert

Arenas

Genaim

et al. 2014

Science of Computer Programming

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Static analysis which takes into account the values of data stored in the heap is considered complex and computationally intractable in practice. Thus, most static analyzers do not keep track of object fields nor of array contents, i.e., they are heap-insensitive. In this article, we propose locality conditions for soundly tracking heap-allocated data in Java (bytecode) programs, by means of ghost non-heap allocated variables. This way, heap-insensitive analysis over the transformed program can infer information on the original heap-allocated data without sacrificing efficiency. If the locality conditions cannot be proven unconditionally, we seek to generate aliasing preconditions which, when they hold in the initial state, guarantee the termination of the program. Experimental results show that we greatly improve the accuracy w.r.t. a heap-insensitive analysis while the overhead introduced is reasonable.

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TVAL+ : TVLA and Value Analyses Together

Cited by 16 publications

References 23 publications

An Abstract Domain Combinator for Separately Conjoining Memory Abstractions

An Abstract Domain Combinator for Separately Conjoining Memory Abstractions

Generic Combination of Heap and Value Analyses in Abstract Interpretation

Conditional termination of loops over heap-allocated data

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