Symbiotic 4: Beyond Reachability

Chalupa, Marek; Vitovská, Martina; Jonas, Martin; Slaby, Jiri; StrejăźEk, Jan

doi:10.1007/978-3-662-54580-5_28

Cited by 16 publications

(10 citation statements)

References 6 publications

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“…12 An abstract domain is called relational when it is capable of preserving information about relationships among various abstract values that appear in the program. 13 In the present paper, we only deal with abstract (symbolic) values. The structure of the program state, that is, the arrangement of the program memory, is taken to be always represented explicitly, i.e., it belongs squarely to the concrete domain.…”

Section: Methodsmentioning

confidence: 99%

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Symbolic Computation via Program Transformation

Lauko

Ročkai

Barnat

2018

Theoretical Aspects of Computing – ICTAC 2018

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Symbolic computation is an important approach in automated program analysis. Most state-of-the-art tools perform symbolic computation as interpreters and directly maintain symbolic data. In this paper, we show that it is feasible, and in fact practical, to use a compilerbased strategy instead. Using compiler tooling, we propose and implement a transformation which takes a standard program and outputs a program that performs semantically equivalent, but partially symbolic, computation. The transformed program maintains symbolic values internally and operates directly on them hence the program can be processed by a tool without support for symbolic manipulation. The main motivation for the transformation is in symbolic verification, but there are many other possible use-cases, including test generation and concolic testing. Moreover using the transformation simplifies tools, since the symbolic computation is handled by the program directly. We have implemented the transformation at the level of LLVM bitcode. The paper includes an experimental evaluation, based on an explicit-state software model checker as a verification backend.

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Section: Methodsmentioning

confidence: 99%

“…Besides standalone usage as a symbolic executor, KLEE has become also a back-end tool for other types of analyses and for verification. For example, the tool Symbiotic [13] combines code instrumentation and slicing with KLEE to detect errors in C programs.…”

Section: Related Workmentioning

confidence: 99%

Symbolic Computation via Program Transformation

Lauko

Ročkai

Barnat

2018

Theoretical Aspects of Computing – ICTAC 2018

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“…With respect to tools, we chose the 10 verification tools participating in all the 4 categories. In this case, these are the tools 2ls (Schrammel and Kroening 2016), CBMC (Kroening and Tautschnig 2014), CPA-Seq (Wendler 2013), DepthK (Rocha et al 2017), ESBMC-incr (Gadelha et al 2018), ESBMC-kind (Gadelha et al 2018), UAutomizer (Heizmann et al 2013), Symbiotic (Chalupa et al 2017), Ukojak (Nutz et al 2015) and UTaipan (Greitschus et al 2017). Since the prediction for a tool pair (V 1 , V 2 ) is the inverse of (V 2 , V 1 ) , we considered only 45 tool pairs 12 during learning.…”

Section: Research Questionsmentioning

confidence: 99%

Algorithm selection for software validation based on graph kernels

et al. 2020

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Algorithm selection is the task of choosing an algorithm from a given set of candidate algorithms when faced with a particular problem instance. Algorithm selection via machine learning (ML) has recently been successfully applied for various problem classes, including computationally hard problems such as SAT. In this paper, we study algorithm selection for software validation, i.e., the task of choosing a software validation tool for a given validation instance. A validation instance consists of a program plus properties to be checked on it. The application of machine learning techniques to this task first of all requires an appropriate representation of software. To this end, we propose a dedicated kernel function, which compares two programs in terms of their similarity, thus making the algorithm selection task amenable to kernel-based machine learning methods. Our kernel operates on a graph representation of source code mixing elements of control-flow and program-dependence graphs with abstract syntax trees. Thus, given two such representations as input, the kernel function yields a real-valued score that can be interpreted as a degree of similarity. We experimentally evaluate our kernel in two learning scenarios, namely a classification and a ranking problem: (1) selecting between a verification and a testing tool for bug finding (i.e., property violation), and (2) ranking several verification tools, from presumably best to worst, for property proving. The evaluation, which is based on data sets from the annual software verification competition SV-COMP, demonstrates our kernel to generalize well and to achieve rather high prediction accuracy, both for the classification and the ranking task.

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“…Symbiotic 7 follows the same basic schema as all previous versions [4,5]: the program to be verified is first instrumented (if needed), then reduced by static program slicing, and finally symbolically executed using Klee [2]. We describe the main modifications since Symbiotic 5 (participating in SV-COMP 2018) as modifications in Symbiotic 6 (competing in 2019) have not been published.…”

Section: Verification Approachmentioning

confidence: 99%

Symbiotic 7: Integration of Predator and More

Chalupa

Jašek

Tomovič

et al. 2020

Tools and Algorithms for the Construction and Analysis of Systems

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Symbiotic 7 brings improvements in all parts of the tool. In particular, we integrated the advanced shape analysis implemented in Predator to our instrumentation process for memory safety checking. Further, we extended our slicer to correctly handle non-terminating programs. This new slicing is applied in termination analysis, where we also added instrumentation for detection of simple cycles in the program state space. The witness generation process changed as well.

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Symbiotic 4: Beyond Reachability

Cited by 16 publications

References 6 publications

Symbolic Computation via Program Transformation

Symbolic Computation via Program Transformation

Algorithm selection for software validation based on graph kernels

Symbiotic 7: Integration of Predator and More

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