Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

We consider a fully SAT-based method of unbounded symbolic model checking based on computing Craig interpolants. In benchmark studies using a set of large industrial circuit verification instances, this method is greatly more efficient than BDD-based symbolic model checking, and compares favorably to some recent SAT-based model checking methods on positive instances.

Interpolation and SAT-based model checking

Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming  is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the  synchronized  keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see  www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important.  Concurrent Programming  is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

Distributed Computing: Fundamentals, Simulations and Advanced Topics

Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called Swift-Hand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L*-based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L*-based testing.

/pdf/guided-gui-testing-of-android-apps-with-minimal-restart-and-3nf6dy3bm0.pdf

Guided GUI testing of android apps with minimal restart and approximate learning

The aim of this chapter is to act as a primer for those wanting to learn about Runtime Verification (RV) We start by providing an overview of the main specification languages used for RV We then introduce the standard terminology necessary to describe the monitoring problem, covering the pragmatic issues of monitoring and instrumentation, and discussing extensively the monitorability problem

/pdf/introduction-to-runtime-verification-5bpjzmx10n.pdf

Introduction to runtime verification

The underlying property, its definition, and representation play a major role when monitoring a system. Having a suitable and convenient framework to express properties is thus a concern for runtime analysis. It is desirable to delineate in this framework the sets of properties for which runtime analysis approaches can be applied to. This paper presents a unified view of runtime verification and enforcement of properties in the Safety-Progress classification. First, we extend the Safety-Progress classification of properties in a runtime context. Second, we characterize the set of properties which can be verified (monitorable properties) and enforced (enforceable properties) at runtime. We propose in particular an alternative definition of “property monitoring” to the one classically used in this context. Finally, for the delineated sets of properties, we define specialized verification and enforcement monitors.

/pdf/what-can-you-verify-and-enforce-at-runtime-19z33rcu4r.pdf

What can you verify and enforce at runtime

Runtime verification is the process of checking a property on a trace of events produced by the execution of a computational system. Runtime verification techniques have recently focused on parametric specifications where events take data values as parameters. These techniques exist on a spectrum inhabited by both efficient and expressive techniques. These characteristics are usually shown to be conflicting - in state-of-the-art solutions, efficiency is obtained at the cost of loss of expressiveness and vice-versa. To seek a solution to this conflict we explore a new point on the spectrum by defining an alternative runtime verification approach. We introduce a new formalism for concisely capturing expressive specifications with parameters. Our technique is more expressive than the currently most efficient techniques while at the same time allowing for optimizations.

/pdf/quantified-event-automata-towards-expressive-and-efficient-3zarlwi7jn.pdf

Quantified Event Automata: Towards Expressive and Efficient Runtime Monitors

This tutorial presents an overview of the field referred as to runtime verification Runtime Verification is the study of algorithms, data structures, and tools focused on analyzing executions of system The performed analysis aims at improving the confidence in systems behavior, either by improving program understanding, or by checking conformance to specifications or algorithms This chapter focuses specifically on checking execution traces against requirements formalized in terms of monitors It is first shown on examples how such monitors can be written using aspect-oriented programming, exemplified by ASPECTJ Subsequently four monitoring systems are illustrated on the same examples The systems cover such formalisms as regular expressions, temporal logics, state machines, and rule-based programming, as well as the distinction between external and internal DSLs

/pdf/a-tutorial-on-runtime-verification-4nomteitzl.pdf

A Tutorial on Runtime Verification

Runtime enforcement is a powerful technique to ensure that a program will respect a given set of properties. We extend previous work on this topic in several directions. Firstly, we propose a generic notion of enforcement monitors based on a memory device and finite sets of control states and enforcement operations. Moreover, we specify their enforcement abilities w.r.t. the general Safety-Progress classification of properties. Furthermore, we propose a systematic technique to produce a monitor from the automaton recognizing a given safety, guarantee, obligation or response property. Finally, we show that this notion of enforcement monitors is more amenable to implementation and encompasses previous runtime enforcement mechanisms.

/pdf/runtime-enforcement-monitors-composition-synthesis-and-3qkvqcximj.pdf

Yliès Falcone

Papers

Introduction to runtime verification

What can you verify and enforce at runtime

Quantified Event Automata: Towards Expressive and Efficient Runtime Monitors

A Tutorial on Runtime Verification

Runtime enforcement monitors: composition, synthesis, and enforcement abilities