Termination Graphs for Java Bytecode

Brockschmidt, Marc; Otto, Carsten; Essen, Christian von; Giesl, Jürgen

doi:10.1007/978-3-642-17172-7_2

Cited by 32 publications

(38 citation statements)

References 17 publications

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“…However, our implementation supports these features, except reflection and multithreading. For further details, see [6][7][8].…”

Section: Handling Irrelevant Cyclesmentioning

confidence: 99%

“…In our earlier papers [6,25] we had another annotation to denote references that may point to non-tree-shaped objects. In the translation to terms later on, all these objects were replaced by fresh variables.…”

Section: Abstract States In Termination Graphsmentioning

confidence: 99%

“…If all list elements had the same value initially, then the iteration either ends with a NullPointerException (if the list is non-cyclic) or because some element is visited for the second time (this is detected by its modified "marked" value). 6 We illustrate the termination graph of visit in Sect. 3.1 and extend our approach in order to prove termination of such marking algorithms in Sect.…”

Section: Proving Termination Via Term Rewritingmentioning

confidence: 99%

“…In [6][7][8]25] we presented an alternative approach for termination of Java via a transformation to TRSs. Like [2,30], we consider Java Bytecode (JBC) to avoid dealing with all language constructs of Java.…”

Section: Introductionmentioning

confidence: 99%

“…In our experiments, we observed that the three considered classes of algorithms capture a large portion of typical programs on cyclic data. For the treatment of (general classes of) other programs, we refer to our earlier papers [6,7,25]. Moreover, in [8] we presented a technique that uses termination graphs to also detect nontermination.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automated Termination Proofs for Java Programs with Cyclic Data

Brockschmidt

Musiol

Otto

et al. 2012

Computer Aided Verification

Self Cite

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Abstract. In earlier work, we developed a technique to prove termination of Java programs automatically: first, Java programs are automatically transformed to term rewrite systems (TRSs) and then, existing methods and tools are used to prove termination of the resulting TRSs. In this paper, we extend our technique in order to prove termination of algorithms on cyclic data such as cyclic lists or graphs automatically. We implemented our technique in the tool AProVE and performed extensive experiments to evaluate its practical applicability.

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“…However, our implementation supports these features, except reflection and multithreading. For further details, see [6][7][8].…”

Section: Handling Irrelevant Cyclesmentioning

confidence: 99%

Section: Abstract States In Termination Graphsmentioning

confidence: 99%

Section: Proving Termination Via Term Rewritingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Termination Proofs for Java Programs with Cyclic Data

Brockschmidt

Musiol

Otto

et al. 2012

Computer Aided Verification

Self Cite

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Lower-Bound Synthesis Using Loop Specialization and Max-SMT

Albert

Genaim

Martin-Martin

et al. 2021

Computer Aided Verification

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This paper presents a new framework to synthesize lower-bounds on the worst-case cost for non-deterministic integer loops. As in previous approaches, the analysis searches for a metering function that under-approximates the number of loop iterations. The key novelty of our framework is the specialization of loops, which is achieved by restricting their enabled transitions to a subset of the inputs combined with the narrowing of their transition scopes. Specialization allows us to find metering functions for complex loops that could not be handled before or be more precise than previous approaches. Technically, it is performed (1) by using quasi-invariants while searching for the metering function, (2) by strengthening the loop guards, and (3) by narrowing the space of non-deterministic choices. We also propose a Max-SMT encoding that takes advantage of the use of soft constraints to force the solver look for more accurate solutions. We show our accuracy gains on benchmarks extracted from the 2020 Termination and Complexity Competition by comparing our results to those obtained by the "Image missing" system.

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