Who is to Blame? Runtime Verification of Distributed Objects with Active Monitors

In Proceedings VORTEX 2018, arXiv:1908.09302International audienceSince distributed software systems are ubiquitous, their correct functioning is crucially important. Static verification is possible in principle, but requires high expertise and effort which is not feasible in many eco-systems. Runtime verification can serve as a lean alternative, where monitoring mechanisms are automatically generated from property specifications, to check compliance at runtime. This paper contributes a practical solution for powerful and flexible runtime verification of distributed, object-oriented applications, via a combination of the runtime verification tool Larva and the active object framework ProActive. Even if Larva supports in itself only the generation of local, sequential monitors, we empower Larva for distributed monitoring by connecting monitors with active objects, turning them into active, communicating monitors. We discuss how this allows for a variety of monitoring architectures. Further, we show how property specifications, and thereby the generated monitors, provide a model that splits the blame between the local object and its environment. While Larva itself focuses on monitoring of control-oriented properties, we use the Larva front-end StaRVOOrS to also capture data-oriented (pre/post) properties in the distributed monitoring. We demonstrate this approach to distributed runtime verification with a case study, a distributed key/value store

A Concurrency-Agnostic Protocol for Multi-Paradigm Concurrent Debugging Tools

Today's complex software systems combine high-level concurrency models. Each model is used to solve a specific set of problems. Unfortunately, debuggers support only the low-level notions of threads and shared memory, forcing developers to reason about these notions instead of the high-level concurrency models they chose. This paper proposes a concurrency-agnostic debugger protocol that decouples the debugger from the concurrency models employed by the target application. As a result, the underlying language runtime can define custom breakpoints, stepping operations, and execution events for each concurrency model it supports, and a debugger can expose them without having to be specifically adapted. We evaluated the generality of the protocol by applying it to SOMns, a Newspeak implementation, which supports a diversity of concurrency models including communicating sequential processes, communicating event loops, threads and locks, fork/join parallelism, and software transactional memory. We implemented 21 breakpoints and 20 stepping operations for these concurrency models. For none of these, the debugger needed to be changed. Furthermore, we visualize all concurrent interactions independently of a specific concurrency model. To show that tooling for a specific concurrency model is possible, we visualize actor turns and message sends separately.Comment: International Symposium on Dynamic Language

Permission-Based Separation Logic for Multithreaded Java Programs

This paper motivates and presents a program logic for reasoning about multithreaded Java-like programs with concurrency primitives such as dynamic thread creation, thread joining and reentrant object monitors. The logic is based on concurrent separation logic. It is the first detailed adaptation of concurrent separation logic to a multithreaded Java-like language. The program logic associates a unique static access permission with each heap location, ensuring exclusive write accesses and ruling out data races. Concurrent reads are supported through fractional permissions. Permissions can be transferred between threads upon thread starting, thread joining, initial monitor entrancies and final monitor exits.\ud This paper presents the basic principles to reason about thread creation and thread joining. It finishes with an outlook how this logic will evolve into a full-fledged verification technique for Java (and possibly other multithreaded languages)

Maintaining consistency in distributed systems

In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability

Semantic labeling of places using information extracted from laser and vision sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments