Ultimate Automizer with Array Interpolation

Heizmann, Matthias; Dietsch, Daniel; Leike, Jan; Musa, Betim; Podelski, Andreas

doi:10.1007/978-3-662-46681-0_43

Cited by 17 publications

(13 citation statements)

References 5 publications

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“…We applied our tool to the test set [3] of Invel [35] tool's input language), and additionally, to a set of non-deterministic numeric programs that we produced ourselves (all test programs are non-terminating, i.e., every program has at least one non-terminating behavior). Table 1 summarizes the results for the Invel and SV-COMP non-terminating programs and compares our tool to 3 existing tools: AProVE [20], Automizer [22], and HipTNT+ [27], and additionally to the authors' previous work on finding universal recurrent sets with forward analysis [8], column "SAS15". For Automizer and HipTNT+, we do not have the results for Invel programs, and for [8], there are no results for SV-COMP benchmarks.…”

Section: Methodsmentioning

confidence: 99%

“…An early analysis by Gupta et al [21] produces proofs of non-termination from lasso-shaped symbolic executions using Farkas' lemma. Au-tomizer [22,23] decomposes the original program into a set of lasso-programs (a stem and a loop with no branches) to separately infer termination or non-termination [28] arguments for them. AProVE [20] implements a range of techniques.…”

Section: Related Workmentioning

confidence: 99%

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Finding Recurrent Sets with Backward Analysis and Trace Partitioning

Bakhirkin

Piterman

2016

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract.We propose an abstract-interpretation-based analysis for recurrent sets. A recurrent set is a set of states from which the execution of a program cannot or might not (as in our case) escape. A recurrent set is a part of a program's nontermination proof (that needs to be complemented by reachability analysis). We find recurrent sets by performing a potentially over-approximate backward analysis that produces an initial candidate. We then perform over-approximate forward analysis on the candidate to check and refine it and ensure soundness. In practice, the analysis relies on trace partitioning that predicts future paths through the program that non-terminating executions will take. Using our technique, we were able to find recurrent sets in many benchmarks found in the literature including some that, to our knowledge, cannot be handled by existing tools. In addition, we note that typically, analyses that search for recurrent sets are applied to linear under-approximations of programs or employ some form of non-approximate numeric reasoning. In contrast, our analysis uses standard abstract-interpretation techniques and is potentially applicable to a larger class of abstract domains (and therefore -programs).

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Finding Recurrent Sets with Backward Analysis and Trace Partitioning

Bakhirkin

Piterman

2016

Tools and Algorithms for the Construction and Analysis of Systems

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“…In addition, we made a comparison with T2 for only 221 loopbased integer programs from the first 3 benchmarks because the tool llvm2KITTeL [18] (which translates C programs into T2's format) cannot properly handle pointers and recursive methods. For AProVE and ULTIMATE, we used their SV-COMP'15 versions, which are described in [43] and [26], respectively. The experiments were performed on an Ubuntu 12.04 machine with the AMD Opteron 6172 (2.1GHz) processor and 64GB of RAM.…”

Section: Methodsmentioning

confidence: 99%

Termination and non-termination specification inference

Qin

Chin

2015

Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Techniques for proving termination and non-termination of imperative programs are usually considered as orthogonal mechanisms. In this paper, we propose a novel mechanism that analyzes and proves both program termination and non-termination at the same time. We first introduce the concept of second-order termination constraints and accumulate a set of relational assumptions on them via a Hoare-style verification. We then solve these assumptions with case analysis to determine the (conditional) termination and nontermination scenarios expressed in some specification logic form. In contrast to current approaches, our technique can construct a summary of terminating and non-terminating behaviors for each method. This enables modularity and reuse for our termination and non-termination proving processes. We have tested our tool on sample programs from a recent termination competition, and compared favorably against state-of-the-art termination analyzers.

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“…We evaluated our tool against 288 terminating C programs from the termination category of SV-COMP 2015. In particular, we compared FuncTion+cdct with other tools from the termination category of SV-COMP 2015 : AProVE [29], FuncTion without cdct [32], HIPTnT+ [22], and Ultimate Automizer [18]. The experiments were performed on a system with a 1.30 GHz 64-bit Dual-Core CPU (Intel i5-4250U) and 4 GB of RAM.…”

Section: Methodsmentioning

confidence: 99%

Conflict-Driven Conditional Termination

D’Silva

Urban

2015

Computer Aided Verification

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Conflict-driven learning, which is essential to the performance of sat and smt solvers, consists of a procedure that searches for a model of a formula, and refutation procedure for proving that no model exists. This paper shows that conflict-driven learning can improve the precision of a termination analysis based on abstract interpretation. We encode non-termination as satisfiability in a monadic second-order logic and use abstract interpreters to reason about the satisfiability of this formula. Our search procedure combines decisions with reachability analysis to find potentially non-terminating executions and our refutation procedure uses a conditional termination analysis. Our implementation extends the set of conditional termination arguments discovered by an existing termination analyzer.

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Ultimate Automizer with Array Interpolation

Cited by 17 publications

References 5 publications

Finding Recurrent Sets with Backward Analysis and Trace Partitioning

Finding Recurrent Sets with Backward Analysis and Trace Partitioning

Termination and non-termination specification inference

Conflict-Driven Conditional Termination

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