289 research outputs found
Property-Driven Fence Insertion using Reorder Bounded Model Checking
Modern architectures provide weaker memory consistency guarantees than
sequential consistency. These weaker guarantees allow programs to exhibit
behaviours where the program statements appear to have executed out of program
order. Fortunately, modern architectures provide memory barriers (fences) to
enforce the program order between a pair of statements if needed. Due to the
intricate semantics of weak memory models, the placement of fences is
challenging even for experienced programmers. Too few fences lead to bugs
whereas overuse of fences results in performance degradation. This motivates
automated placement of fences. Tools that restore sequential consistency in the
program may insert more fences than necessary for the program to be correct.
Therefore, we propose a property-driven technique that introduces
"reorder-bounded exploration" to identify the smallest number of program
locations for fence placement. We implemented our technique on top of CBMC;
however, in principle, our technique is generic enough to be used with any
model checker. Our experimental results show that our technique is faster and
solves more instances of relevant benchmarks as compared to earlier approaches.Comment: 18 pages, 3 figures, 4 algorithms. Version change reason : new set of
results and publication ready version of FM 201
Sound Static Deadlock Analysis for C/Pthreads (Extended Version)
We present a static deadlock analysis approach for C/pthreads. The design of
our method has been guided by the requirement to analyse real-world code. Our
approach is sound (i.e., misses no deadlocks) for programs that have defined
behaviour according to the C standard, and precise enough to prove
deadlock-freedom for a large number of programs. The method consists of a
pipeline of several analyses that build on a new context- and thread-sensitive
abstract interpretation framework. We further present a lightweight dependency
analysis to identify statements relevant to deadlock analysis and thus speed up
the overall analysis. In our experimental evaluation, we succeeded to prove
deadlock-freedom for 262 programs from the Debian GNU/Linux distribution with
in total 2.6 MLOC in less than 11 hours
Chaining Test Cases for Reactive System Testing (extended version)
Testing of synchronous reactive systems is challenging because long input
sequences are often needed to drive them into a state at which a desired
feature can be tested. This is particularly problematic in on-target testing,
where a system is tested in its real-life application environment and the time
required for resetting is high. This paper presents an approach to discovering
a test case chain---a single software execution that covers a group of test
goals and minimises overall test execution time. Our technique targets the
scenario in which test goals for the requirements are given as safety
properties. We give conditions for the existence and minimality of a single
test case chain and minimise the number of test chains if a single test chain
is infeasible. We report experimental results with a prototype tool for C code
generated from Simulink models and compare it to state-of-the-art test suite
generators.Comment: extended version of paper published at ICTSS'1
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
Lost in Abstraction: Monotonicity in Multi-Threaded Programs (Extended Technical Report)
Monotonicity in concurrent systems stipulates that, in any global state,
extant system actions remain executable when new processes are added to the
state. This concept is not only natural and common in multi-threaded software,
but also useful: if every thread's memory is finite, monotonicity often
guarantees the decidability of safety property verification even when the
number of running threads is unknown. In this paper, we show that the act of
obtaining finite-data thread abstractions for model checking can be at odds
with monotonicity: Predicate-abstracting certain widely used monotone software
results in non-monotone multi-threaded Boolean programs - the monotonicity is
lost in the abstraction. As a result, well-established sound and complete
safety checking algorithms become inapplicable; in fact, safety checking turns
out to be undecidable for the obtained class of unbounded-thread Boolean
programs. We demonstrate how the abstract programs can be modified into
monotone ones, without affecting safety properties of the non-monotone
abstraction. This significantly improves earlier approaches of enforcing
monotonicity via overapproximations
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