Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

ion Refinement for Emptiness Checking of Alternating Data Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Radu Iosif and Xiao Xu Revisiting Enumerative Instantiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Andrew Reynolds, Haniel Barbosa, and Pascal Fontaine A Non-linear Arithmetic Procedure for Control-Command Software Verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Pierre Roux, Mohamed Iguernlala, and Sylvain Conchon Security and Reactive Systems Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Milan Češka, Vojtěch Havlena, Lukáš Holík, Ondřej Lengál, and Tomáš Vojnar Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Andreas Katis, Grigory Fedyukovich, Huajun Guo, Andrew Gacek, John Backes, Arie Gurfinkel, and Michael W. Whalen RVHyper: A Runtime Verification Tool for Temporal Hyperproperties . . . . . 194 Bernd Finkbeiner, Christopher Hahn, Marvin Stenger, and Leander Tentrup The Refinement Calculus of Reactive Systems Toolset . . . . . . . . . . . . . . . . 201 Iulia Dragomir, Viorel Preoteasa, and Stavros Tripakis Static and Dynamic Program Analysis TESTOR: A Modular Tool for On-the-Fly Conformance Test Case Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Lina Marsso, Radu Mateescu, and Wendelin Serwe Optimal Dynamic Partial Order Reduction with Observers . . . . . . . . . . . . . . 229 Stavros Aronis, Bengt Jonsson, Magnus Lång, and Konstantinos Sagonas Structurally Defined Conditional Data-Flow Static Analysis . . . . . . . . . . . . . 249 Elena Sherman and Matthew B. Dwyer Geometric Nontermination Arguments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jan Leike and Matthias Heizmann Hybrid and Stochastic Systems Efficient Dynamic Error Reduction for Hybrid Systems Reachability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Stefan Schupp and Erika Ábrahám AMT 2.0: Qualitative and Quantitative Trace Analysis with Extended Signal Temporal Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Dejan Ničković, Olivier Lebeltel, Oded Maler, Thomas Ferrère, and Dogan Ulus Multi-cost Bounded Reachability in MDP . . . . . . . . . . . . . . . . . . . . . . . . . 320 Arnd Hartmanns, Sebastian Junges, Joost-Pieter Katoen, and Tim Quatmann A Statistical Model Checker for Nondeterminism and Rare Events . . . . . . . . 340 Carlos E. Budde, Pedro R. D’Argenio, Arnd Hartmanns, and Sean Sedwards XVIII

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Tools and Algorithms for the Construction and Analysis of Systems : 24th International Conference, TACAS 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceedings, Part I

We present $L^{\#}$, a new and simple approach to active automata learning. Instead of focusing on equivalence of observations, like the $L^{\ast}$ algorithm and its descendants, $L^{\#}$ takes a different perspective: it tries to establish apartness, a constructive form of inequality. $L^{\#}$ does not require auxiliary notions such as observation tables or discrimination trees, but operates directly on tree-shaped automata. $L^{\#}$ has the same asymptotic query and symbol complexities as the best existing learning algorithms, but we show that adaptive distinguishing sequences can be naturally integrated to boost the performance of $L^{\#}$ in practice. Experiments with a prototype implementation, written in Rust, suggest that $L^{\#}$ outperforms existing algorithms.

A New Approach for Active Automata Learning Based on Apartness.

Software model checking as well as runtime verification are verification techniques which are widely used for checking temporal properties of software systems. Even though they are property verification techniques, their common usage in practice is in "bug finding", that is, finding violations of temporal properties. Motivated by this observation and leveraging the recent progress in fuzzing, we build a greybox fuzzing framework to find violations of Linear-time Temporal Logic (LTL) properties. 

/pdf/linear-time-temporal-logic-guided-greybox-fuzzing-2vk18gu2.pdf

Linear-time temporal logic guided greybox fuzzing

High-tech companies are struggling today with the maintenance of legacy software Legacy software is vital to many organizations as it contains the important business logic To facilitate maintenance of legacy software, a comprehensive understanding of the software’s behavior is essential In terms of component-based software engineering, it is necessary to completely understand the behavior of components in relation to their interfaces, ie, their interface protocols, and to preserve this behavior during the maintenance activities of the components For this purpose, we present an approach to infer the interface protocols of software components from the behavioral models of those components, learned by a blackbox technique called active (automata) learning To validate the learned results, we applied our approach to the software components developed with model-based engineering so that equivalence can be checked between the learned models and the reference models, ensuring the behavioral relations are preserved Experimenting with components having reference models and performing equivalence checking builds confidence that applying active learning technique to reverse engineer legacy software components, for which no reference models are available, will also yield correct results To apply our approach in practice, we present an automated framework for conducting active learning on a large set of components and deriving their interface protocols Using the framework, we validated our methodology by applying active learning on 202 industrial software components, out of which, interface protocols could be successfully derived for 156 components within our given time bound of 1 h for each component

/pdf/interface-protocol-inference-to-aid-understanding-legacy-4194otn72w.pdf

Interface protocol inference to aid understanding legacy software components

More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities. For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison between learned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned results, thus developing confidence in applying the active learning technique to reverse engineer the legacy software components in the future. © 2018 CEUR-WS. All rights reserved.

/pdf/interface-protocol-inference-to-aid-understanding-legacy-2xj5xuh8wt.pdf

Applying Model-Driven Engineering can improve development efficiency. But gaining such benefits for legacy software requires models, and creating them manually is both laborious and error prone. Active automata learning has the potential to make it cost-effective, but practitioners face practical challenges applying it to software components of industrial cyber-physical systems. To overcome these challenges, we present a framework to learn the behavior of component-based software with a client/server architecture, focusing on interfacing isolated component code with an active learning tool. An essential part of the framework is an interfacing protocol that provides a structured way of handling the (a)synchronous communications between the component and learning tool. Our main contribution is the systematic derivation of such interfacing protocols for component-based software, which we demonstrate on the software architecture of ASML, a leading company in developing lithography machines. Through several practical case studies we show that our semi-automatic approach enables setting up a learning environment to learn component behaviors within hours. The protocol’s responsibilities and the way it handles different communication types apply to component-based software in general. Our framework could thus be adapted for companies with similar software architectures. 

/pdf/a-systematic-approach-for-interfacing-component-based-1hh4dt88.pdf

A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool

Modern high-tech industry is dealing with the maintenance of complex software systems today which consist of a large number of interconnected software components. Many of these components become legacy over years due to lack of documentation and unavailability of original developers. Several techniques are available in literature to retrieve the behavioral models from the existing software. Among those, the dynamic analysis techniques analyze the actual execution of the software, either via execution traces (passive learning), or by interaction with the software components (active learning). These techniques cannot guarantee alone to learn the complete and correct software behavior due to the limitations of each technique. We present an approach to aid active learning technique with software logs (execution traces) and passive learning result to increase the behavioral coverage of learned models.

Refining active learning to increase behavioral coverage

From airports to banks, healthcare, space crafts, and even amazon services, technology impacts almost every aspect of today’s life. If wrongdoings occur within or in relation to technology, they can have big implications on individuals, groups of people, or society as a whole. Whistleblowers are insiders who expose such wrongdoings— eventually stopping misconducts, such as fraud, endangerment to public health and safety, or damage to the environment. Twitter is a microblogging service that allows millions of users to share their views with people distributed all over the world on a daily basis. Tweets have the potential to contain useful information about whistleblowing in tech, from the general public and whistleblowers. However, until now this point has not been researched.To fill this gap, we conducted an exploratory study on technology-related whistleblowing tweets by manually analysing tweets, utilising descriptive statistics, and machine learning techniques. We mined 7,400 tweets from whistleblowers themselves, as well as news and opinions about certain whistleblowers and whistleblowing cases. Although our results show that only 30% of the tweets in our sample dataset (obtained through specific search terms) contained relevant information about whistleblowing in technology, our analysis shows that tweets provide valuable information for both researchers and companies to understand the public opinion regarding whistleblowing cases. Furthermore, we found that machine learning techniques are promising means for extracting information about whistleblowing in tech from the vast stream of tweets.

Whistleblowing and Tech on Twitter

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Interface protocol inference to aid understanding legacy software components

Interface protocol inference to aid understanding legacy software components

A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool

Refining active learning to increase behavioral coverage

Whistleblowing and Tech on Twitter