Program Analysis for Overlaid Data Structures

Lee, Oukseh; Yang, Hongseok; Petersen, Rasmus Lerchedahl

doi:10.1007/978-3-642-22110-1_48

Cited by 22 publications

(27 citation statements)

References 15 publications

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“…Likewise, Calcagno et al [7] describes an analysis for determining lock invariants with only linked lists. Lee et al [22] presents a shape analysis specifically tailored to overlaid data structures. In the matching logic framework, a set of predicates is typically assumed for program verification [31].…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Shape Analysis via Second-Order Bi-Abduction

Gherghina

Qin

et al. 2014

Lecture Notes in Computer Science

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Abstract. We present a new modular shape analysis that can synthesize heap memory specification on a per method basis. We rely on a second-order biabduction mechanism that can give interpretations to unknown shape predicates. There are several novel features in our shape analysis. Firstly, it is grounded on second-order bi-abduction. Secondly, we distinguish unknown pre-predicates in pre-conditions, from unknown post-predicates in post-condition; since the former may be strengthened, while the latter may be weakened. Thirdly, we provide a new heap guard mechanism to support more precise preconditions for heap specification. Lastly, we formalise a set of derivation and normalization rules to give concise definitions for unknown predicates. Our approach has been proven sound and is implemented on top of an existing automated verification system. We show its versatility in synthesizing a wide range of intricate shape specifications.

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Section: Related Work and Conclusionmentioning

confidence: 99%

Shape Analysis via Second-Order Bi-Abduction

Gherghina

Qin

et al. 2014

Lecture Notes in Computer Science

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“…In particular, [14] presents an approach that uses classical conjunction together with zone variables to relate corresponding regions. Moreover, [11] combines formulae by distinguishing per-field and per-object separating conjunctions.…”

Section: Related Workmentioning

confidence: 99%

“…While there exist static analyses for the most common data structures such as lists, trees, or even overlaid lists and trees [10,4,14,11], it is uncommon for static analyses to efficiently support all of these simultaneously. For instance, consider the code fragment of Fig.…”

Section: Introductionmentioning

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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“…It would be interesting to try and recast their proofs in our setting. Lee et al devised an automatic analysis for threaded trees that instruments the results of separate analyses for lists and for trees [LYP11].…”

Section: Related Workmentioning

confidence: 99%

The ramifications of sharing in data structures

HoborAquinas

Villard

2013

SIGPLAN Not.

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Programs manipulating mutable data structures with intrinsic sharing present a challenge for modular verification. Deep aliasing inside data structures dramatically complicates reasoning in isolation over parts of these objects because changes to one part of the structure (say, the left child of a dag node) can affect other parts (the right child or some of its descendants) that may point into it. The result is that finding intuitive and compositional proofs of correctness is usually a struggle. We propose a compositional proof system that enables local reasoning in the presence of sharing.While the AI "frame problem" elegantly captures the reasoning required to verify programs without sharing, we contend that natural reasoning about programs with sharing instead requires an answer to a different and more challenging AI problem, the "ramification problem": reasoning about the indirect consequences of actions. Accordingly, we present a RAMIFY proof rule that attacks the ramification problem head-on and show how to reason with it. Our framework is valid in any separation logic and permits sound compositional and local reasoning in the context of both specified and unspecified sharing. We verify the correctness of a number of examples, including programs that manipulate dags, graphs, and overlaid data structures in nontrivial ways.

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Program Analysis for Overlaid Data Structures

Cited by 22 publications

References 15 publications

Shape Analysis via Second-Order Bi-Abduction

Shape Analysis via Second-Order Bi-Abduction

An Abstract Domain Combinator for Separately Conjoining Memory Abstractions

The ramifications of sharing in data structures

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