AProVE: Termination and Memory Safety of C Programs

Ströder, Thomas; Aschermann, Cornelius; Frohn, Florian; Hensel, Jera; Giesl, Jürgen

doi:10.1007/978-3-662-46681-0_32

Cited by 13 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A very short description of AProVE's use for termination analysis of C programs was published in [59] and a preliminary version of parts of this paper was published in [42]. The present paper extends [42] substantially:…”

mentioning

confidence: 67%

Analyzing Program Termination and Complexity Automatically with AProVE

et al. 2016

Self Cite

View full text Add to dashboard Cite

In this system description, we present the tool AProVE for automatic termination and complexity proofs of Java, C, Haskell, Prolog, and rewrite systems. In addition to classical term rewrite systems (TRSs), AProVE also supports rewrite systems containing built-in integers (int-TRSs). To analyze programs in high-level languages, AProVE automatically converts them to (int-)TRSs. Then, a wide range of techniques is employed to prove termination and to infer complexity bounds for the resulting rewrite systems. The generated proofs can be exported to check their correctness using automatic certifiers. To use AProVE in software construction, we present a corresponding plug-in for the popular Eclipse software development environment.

show abstract

mentioning

confidence: 67%

Analyzing Program Termination and Complexity Automatically with AProVE

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

SIGPLAN Not.

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

AProVE: Termination and Memory Safety of C Programs

Cited by 13 publications

References 5 publications

Analyzing Program Termination and Complexity Automatically with AProVE

Analyzing Program Termination and Complexity Automatically with AProVE

Termination and non-termination specification inference

Conflict-Driven Conditional Termination

Contact Info

Product

Resources

About