1,684 research outputs found
Termination Analysis with Compositional Transition Invariants
Modern termination provers rely on a safety checker to construct disjunctively well-founded transition invariants. This safety check is known to be the bottleneck of the procedure. We present an alternative algorithm that uses a light-weight check based on transitivity of ranking relations to prove program termination. We provide an exper-imental evaluation over a set of 87 Windows drivers, and demonstrate that our algorithm is often able to conclude termination by examining only a small fraction of the program. As a consequence, our algorithm is able to outperform known approaches by multiple orders of magnitude
Proving termination through conditional termination
We present a constraint-based method for proving conditional termination of integer programs. Building on this, we construct a framework to prove (unconditional) program termination using a powerful mechanism to combine conditional termination proofs. Our key insight is that a conditional termination proof shows termination for a subset of program execution states which do not need to be considered in the remaining analysis. This facilitates more effective termination as well as non-termination analyses, and allows handling loops with different execution phases naturally. Moreover, our method can deal with sequences of loops compositionally. In an empirical evaluation, we show that our implementation VeryMax outperforms state-of-the-art tools on a range of standard benchmarks.Peer ReviewedPostprint (author's final draft
Proving Non-Termination via Loop Acceleration
We present the first approach to prove non-termination of integer programs
that is based on loop acceleration. If our technique cannot show
non-termination of a loop, it tries to accelerate it instead in order to find
paths to other non-terminating loops automatically. The prerequisites for our
novel loop acceleration technique generalize a simple yet effective
non-termination criterion. Thus, we can use the same program transformations to
facilitate both non-termination proving and loop acceleration. In particular,
we present a novel invariant inference technique that is tailored to our
approach. An extensive evaluation of our fully automated tool LoAT shows that
it is competitive with the state of the art
Proving Termination Starting from the End
We present a novel technique for proving program termination which introduces
a new dimension of modularity. Existing techniques use the program to
incrementally construct a termination proof. While the proof keeps changing,
the program remains the same. Our technique goes a step further. We show how to
use the current partial proof to partition the transition relation into those
behaviors known to be terminating from the current proof, and those whose
status (terminating or not) is not known yet. This partition enables a new and
unexplored dimension of incremental reasoning on the program side. In addition,
we show that our approach naturally applies to conditional termination which
searches for a precondition ensuring termination. We further report on a
prototype implementation that advances the state-of-the-art on the grounds of
termination and conditional termination.Comment: 16 page
Termination Analysis by Learning Terminating Programs
We present a novel approach to termination analysis. In a first step, the
analysis uses a program as a black-box which exhibits only a finite set of
sample traces. Each sample trace is infinite but can be represented by a finite
lasso. The analysis can "learn" a program from a termination proof for the
lasso, a program that is terminating by construction. In a second step, the
analysis checks that the set of sample traces is representative in a sense that
we can make formal. An experimental evaluation indicates that the approach is a
potentially useful addition to the portfolio of existing approaches to
termination analysis
Loop summarization using state and transition invariants
This paper presents algorithms for program abstraction based on the principle of loop summarization, which, unlike traditional program approximation approaches (e.g., abstract interpretation), does not employ iterative fixpoint computation, but instead computes symbolic abstract transformers with respect to a set of abstract domains. This allows for an effective exploitation of problem-specific abstract domains for summarization and, as a consequence, the precision of an abstract model may be tailored to specific verification needs. Furthermore, we extend the concept of loop summarization to incorporate relational abstract domains to enable the discovery of transition invariants, which are subsequently used to prove termination of programs. Well-foundedness of the discovered transition invariants is ensured either by a separate decision procedure call or by using abstract domains that are well-founded by construction. We experimentally evaluate several abstract domains related to memory operations to detect buffer overflow problems. Also, our light-weight termination analysis is demonstrated to be effective on a wide range of benchmarks, including OS device driver
Learning to Prove Safety over Parameterised Concurrent Systems (Full Version)
We revisit the classic problem of proving safety over parameterised
concurrent systems, i.e., an infinite family of finite-state concurrent systems
that are represented by some finite (symbolic) means. An example of such an
infinite family is a dining philosopher protocol with any number n of processes
(n being the parameter that defines the infinite family). Regular model
checking is a well-known generic framework for modelling parameterised
concurrent systems, where an infinite set of configurations (resp. transitions)
is represented by a regular set (resp. regular transducer). Although verifying
safety properties in the regular model checking framework is undecidable in
general, many sophisticated semi-algorithms have been developed in the past
fifteen years that can successfully prove safety in many practical instances.
In this paper, we propose a simple solution to synthesise regular inductive
invariants that makes use of Angluin's classic L* algorithm (and its variants).
We provide a termination guarantee when the set of configurations reachable
from a given set of initial configurations is regular. We have tested L*
algorithm on standard (as well as new) examples in regular model checking
including the dining philosopher protocol, the dining cryptographer protocol,
and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and
German). Our experiments show that, despite the simplicity of our solution, it
can perform at least as well as existing semi-algorithms.Comment: Full version of FMCAD'17 pape
Compositional Verification for Timed Systems Based on Automatic Invariant Generation
We propose a method for compositional verification to address the state space
explosion problem inherent to model-checking timed systems with a large number
of components. The main challenge is to obtain pertinent global timing
constraints from the timings in the components alone. To this end, we make use
of auxiliary clocks to automatically generate new invariants which capture the
constraints induced by the synchronisations between components. The method has
been implemented in the RTD-Finder tool and successfully experimented on
several benchmarks
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