Section: New Results

Verification techniques

Participants : Daniel Hirschkoff, Elena Giachino, Cosimo Laneve, Davide Sangiorgi.

Deadlock detection

In [22] we present a framework for statically detecting deadlocks in a concurrent object-oriented language with asynchronous method calls and cooperative scheduling of method activations. Since this language features recursion and dynamic resource creation, deadlock detection is extremely complex and state-of-the-art solutions either give imprecise answers or do not scale. In order to augment precision and scalability we propose a modular framework that allows several techniques to be combined. The basic component of the framework is a front-end inference algorithm that extracts abstract behavioural descriptions of methods, called contracts, which retain resource dependency information. This component is integrated with a number of possible different back-ends that analyze contracts and derive deadlock information. As a proof-of-concept, we discuss two such back-ends: (i) an evaluator that computes a fixpoint semantics and (ii) an evaluator using abstract model checking.

In [36] we study deadlock detection in an actor model with wait-by-necessity synchronizations, a lightweight technique that synchronizes invocations when the corresponding values are strictly needed. This approach relies on the use of futures that are not given an explicit Future type. The approach we adopt allows for the implicit synchronization on the availability of some value (where the producer of the value might be decided at runtime), whereas previous work allowed only explicit synchronization on the termination of a well-identified request. This way we are able to analyze the data-flow synchronization inherent to languages that feature wait-by-necessity. We provide a type-system and a solver inferring the type of a program so that deadlocks can be identified statically. As a consequence we can automatically verify the absence of deadlocks in actor programs with wait-by-necessity synchronizations.

Service Level Agreement

There is a gap between run-time service behaviours and the contracted quality expectations with the customers that is due to the informal nature of service level agreements. In [41] we explain how to bridge the gap by formalizing service level agreements with metric functions. We therefore discuss an end-to-end analysis flow that can either statically verify if a service code complies with a metric function or use run-time monitoring systems to report possible misbehaviours. In both cases, our approach provides a feedback loop to fix and improve the metrics and eventually the resource configurations of the service itself.