InvGen: An Efficient Invariant Generator

Gupta, Ashutosh; Rybalchenko, Andrey

doi:10.1007/978-3-642-02658-4_48

Cited by 115 publications

(95 citation statements)

References 9 publications

(13 reference statements)

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“…We evaluate our technique by comparing the quality of the loop invariants obtained from two publicly available invariant generation tools, Interproc [19] and InvGen [6], before and after decomposing multi-phase loops into a sequence of single-phase loops. Interproc is an abstract interpretation-based tool that implements the interval, octagon, and polyhedra abstract domains using the Apron [9] and Fixpoint [20] libraries.…”

Section: Methodsmentioning

confidence: 99%

“…These approaches include (i) template-based techniques, such as [26,6,27], (ii) techniques based on predicate abstraction such as [11,12,13,22,5], and (iii) techniques based on probabilistic inference [4]. While some of these approaches are, in principle, capable of discovering precise invariants in loops exhibiting multiple phases, they are significantly more complicated, less efficient, and less widely-used than standard abstract interpretation-based techniques for generating conjunctive numeric invariants such as [1,2,28].…”

Section: Related Workmentioning

confidence: 99%

“…While relatively rare, obtaining accurate invariants for such loops is still important for successful verification, as imprecision in the analysis of even a small part of a program tends to spread, often reducing analysis precision for much of, or even the entire, program. As a result, a number of previous efforts have proposed techniques for inferring disjunctive invariants [4,5,6,7,8]. While there is considerable diversity in the approaches taken, the proposed disjunctive invariant generation techniques are considerably more involved than the more straightforward conjunctive case.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplifying Loop Invariant Generation Using Splitter Predicates

Sharma

Dillig

et al. 2011

Computer Aided Verification

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Abstract. We present a novel static analysis technique that substantially improves the quality of invariants inferred by standard loop invariant generation techniques. Our technique decomposes multi-phase loops, which require disjunctive invariants, into a semantically equivalent sequence of single-phase loops, each of which requires simple, conjunctive invariants. We define splitter predicates which are used to identify phase transitions in loops, and we present an algorithm to find useful splitter predicates that enable the phase-reducing transformation. We show experimentally on a set of representative benchmarks from the literature and real code examples that our technique substantially increases the quality of invariants inferred by standard invariant generation techniques. Our technique is conceptually simple, easy to implement, and can be integrated into any automatic loop invariant generator.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simplifying Loop Invariant Generation Using Splitter Predicates

Sharma

Dillig

et al. 2011

Computer Aided Verification

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“…Classical abstract interpretation [32] and its applications such as automated assertion discovery [33,34,35,36] mainly focus on finding numerical program properties. Our work is complementary to these advances as our focus is more on refining specifications for heap-manipulating programs.…”

Section: Related Workmentioning

confidence: 99%

Automatically refining partial specifications for heap-manipulating programs

Qin

Luo

et al. 2014

Science of Computer Programming

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Automatically verifying heap-manipulating programs is a challenging task, especially when dealing with complex data structures with strong invariants, such as sorted lists and AVL/red-black trees. The verification process can greatly benefit from human assistance through specification annotations, but this process requires intellectual effort from users and is error-prone. In this paper, we propose a new approach to program verification that allows users to provide only partial specification to methods. Our approach will then refine the given annotation into a more complete specification by discovering missing constraints. The discovered constraints may involve both numerical and multi-set properties that could be later confirmed or revised by users. We further augment our approach by requiring partial specification to be given only for primary methods. Specifications for loops and auxiliary methods can then be systematically discovered by our augmented mechanism, with the help of information propagated from the primary methods. Our work is aimed at verifying beyond shape properties, with the eventual goal of analysing full functional properties for pointer-based data structures. Initial experiments have confirmed that we can automatically refine partial specifications with non-trivial constraints, thus making it easier for users to handle specifications with richer properties.

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“…Also related but different from ours is work in the areas of invariant generation and specification mining, which extract properties of a program or system model, such as invariants [22,13,23], temporal logic formulas [27,33] or non-deterministic finite automata [6].…”

Section: Introductionmentioning

confidence: 99%

Learning Moore Machines from Input-Output Traces

Giantamidis

Tripakis

2016

Lecture Notes in Computer Science

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Abstract. The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample.

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InvGen: An Efficient Invariant Generator

Abstract: In this paper we present InvGen, an automatic linear arithmetic invariant generator for imperative programs. InvGen's unique feature is in its use of dynamic analysis to make invariant generation order of magnitude more efficient.1 See [5] for the syntax of transition relations.

Cited by 115 publications

References 9 publications

Simplifying Loop Invariant Generation Using Splitter Predicates

Simplifying Loop Invariant Generation Using Splitter Predicates

Automatically refining partial specifications for heap-manipulating programs

Learning Moore Machines from Input-Output Traces

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