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Cấu trúc

  • Preface

  • Organization

  • Contents

  • Introduction to the First Issue of FoMaC

    • 1 Motivation

    • 2 Individual Contributions

    • 3 Conclusion

    • References

  • Knowledge Management for Inclusive System Evolution

    • 1 Change Is Not Always Just Change

    • 2 Change at Large

    • 3 Change and Simplicity

    • 4 Simplifying Knowledge Management

    • 5 Simplifying System Design

      • 5.1 The Issues with Code

      • 5.2 The Issues with Models

      • 5.3 Living Models for Communication Across the IT Gap

    • 6 Simplifying Evolution Management

    • 7 Conclusions

    • References

  • Archimedean Points: The Essence for Mastering Change

    • 1 Motivation

    • 2 Constraint-Oriented System Development

    • 3 The Cinco Meta Tooling Suite

    • 4 Application Scenario: From Petri Nets to BPMN

      • 4.1 Tool Basics

      • 4.2 Bipartiteness as Archimedean Point

      • 4.3 Place Capacities

      • 4.4 Subnets as Components

      • 4.5 Realization of BPMN Features

    • 5 Related Work

    • 6 Conclusion

    • References

  • Model Patterns

    • 1 Introduction

    • 2 Definitions

      • 2.1 Models

      • 2.2 Constraints

      • 2.3 Metamodels

    • 3 Patterns

      • 3.1 Example Patterns

      • 3.2 Pattern Instantiation

    • 4 Usage Scenarios

      • 4.1 Refinement

      • 4.2 Discovery

      • 4.3 Evolution and Migration

    • 5 Conclusion

      • 5.1 Evaluation

      • 5.2 Future Work

      • 5.3 Related Work

    • References

  • Verified Change

    • 1 Introduction

    • 2 The K Language

    • 3 Change

      • 3.1 Behavior

      • 3.2 Refinement

    • 4 Related Work

    • 5 Conclusion

    • A K Grammar

    • References

  • Good Change and Bad Change: An Analysis Perspective on Software Evolution

    • Abstract

    • 1 Introduction

    • 2 Software Architecture Evolution and Evaluation

      • 2.1 Software Architecture

      • 2.2 Evolving Architectures

      • 2.3 The Two Techniques and Their Usage

      • 2.4 The SAVE Tool

    • 3 Variant Analysis

      • 3.1 Set-Based Similarity Concept

      • 3.2 Hierarchical Set Similarity Model

      • 3.3 Visualizations

    • 4 Case Study

      • 4.1 The System Under Study: The TSAFE Product Line

      • 4.2 The Architecture of TSAFE

      • 4.3 Basic TSAFE Variants C and D and the Change Request

      • 4.4 The Abstract Client Template Pattern

    • 5 Variant Analysis of TSAFE

    • 6 Tracing Software Evolution

    • 7 Conclusion

    • Acknowledgements

    • References

  • Compositional Model-Based System Design and Other Foundations for Mastering Change

    • 1 Systems and Models of Systems

    • 2 System Design

    • 3 Compositionality

    • 4 Beyond Compositionality

    • 5 Conclusions

    • References

  • Proof Repositories for Compositional Verification of Evolving Software Systems

    • 1 Introduction

    • 2 A Framework for Contract-Based Verification

      • 2.1 The Programming Model

      • 2.2 Contract-Based Verification

    • 3 Abstract Method Calls

    • 4 A Proof Repository

    • 5 Examples: Integration with Structuring Concepts

      • 5.1 Class Inheritance and Behavioral Subtyping

      • 5.2 Delta-Oriented Programming

    • 6 Initial Experiments

    • 7 Related Work

    • 8 Conclusion

    • References

  • Statistical Model Checking with Change Detection

    • 1 Introduction and Motivations

    • 2 Related Work

    • 3 Systems and Problems

      • 3.1 Quantitative and Optimization Problems

      • 3.2 Change Detection Problem

    • 4 A Statistical Model Checking Approach

      • 4.1 Quantitative Verification

      • 4.2 Change Detection with CUSUM

    • 5 Plasma Lab and Simulink Integration

      • 5.1 On Plasma Lab

      • 5.2 On Integrating Plasma Lab Within Simulink

    • 6 A Pig Shed Case Study

      • 6.1 Quantitative Verification and Optimization

      • 6.2 Change Detection: Detection and Calibration

    • 7 Conclusion

    • References

  • Collective Autonomic Systems: Towards Engineering Principles and Their Foundations

    • 1 Introduction

    • 2 The Robot Rescue Scenario

    • 3 The Development Life Cycle of Collective Autonomic Systems

    • 4 Design Loop

      • 4.1 Use Probabilistic Goal- and Utility-Based Techniques

      • 4.2 Characterize and Design Adaptation and Awareness

    • 5 Runtime Loop

      • 5.1 Exploit Interaction

      • 5.2 Perform Reasoning, Learning and Planning

    • 6 Evolution Loop

      • 6.1 Consider Interplay of Static and Dynamic Knowledge

      • 6.2 Enable Evolutionary Feedback and Manage Evolution

    • 7 Engineering Principles for All Loops

      • 7.1 Perform Simulation and Analysis

      • 7.2 Consider Societies of Systems

    • 8 Conclusions

    • References

  • Continuous Collaboration for Changing Environments

    • 1 Introduction

    • 2 Motivation

      • 2.1 The ``Robot Rescue Force'' Example

      • 2.2 Finding an Approach for the Example

    • 3 Foundations

      • 3.1 PAC Learning

      • 3.2 Domain Knowledge and Inductive Bias

      • 3.3 Distributed and Common Knowledge

      • 3.4 Selecting Prediction Rules and Changing Environments

    • 4 Continuous Collaboration

      • 4.1 Teacher/Student Learning

      • 4.2 Extended Behavior Trees (XBTs)

      • 4.3 Life-Cycle Integration in Continuous Collaboration

    • 5 Experiments

    • 6 Related Work

    • 7 Conclusion and Future Work

    • References

  • Issues on Software Quality Models for Mastering Change

    • 1 Introduction

    • 2 Software Quality Models

      • 2.1 Descriptive Software Quality Models

      • 2.2 Generating Software Quality Models

      • 2.3 Predictive Software Quality Models

    • 3 Discussion of Current Issues

      • 3.1 Creation and Maintenance of Models

      • 3.2 Traceability Between Quality Models and Unstructured Artifacts

      • 3.3 Support for Extra-Functional Aspects

      • 3.4 Integration of Software Analytics and Runtime Information

      • 3.5 Balance Between Quality and Risk

      • 3.6 Process Integration

      • 3.7 Justification by Empirical Evidence

    • 4 Conclusion

    • References

  • Traceability Types for Mastering Change in Collaborative Software Quality Management

    • 1 Introduction

    • 2 State-of-the-Art

    • 3 Collaborative Software Quality Management

      • 3.1 The Living Models Framework

      • 3.2 Collaborative Quality Management Services

    • 4 Traceability Categories

    • 5 Traceability Supporting Collaborative Software Quality Management Services

      • 5.1 Traceability Support for Knowledge Engineering Services

      • 5.2 Traceability Support for Analysis Services

      • 5.3 Traceability Support for Change-Driven Engineering Services

    • 6 Conclusion

    • References

  • Author Index

Nội dung

Journal Subline LNCS 9960 Transactions on Foundations for Mastering Change I Bernhard Steffen Editor-in-Chief 123 Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zurich, Switzerland John C Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany 9960 More information about this series at http://www.springer.com/series/15545 Bernhard Steffen (Ed.) Transactions on Foundations for Mastering Change I 123 Editor-in-Chief Bernhard Steffen TU Dortmund Dortmund Germany ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-319-46507-4 ISBN 978-3-319-46508-1 (eBook) DOI 10.1007/978-3-319-46508-1 Library of Congress Control Number: 2016951710 © Springer International Publishing AG 2016 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface The goal of the LNCS–Transactions on Foundations for Mastering Change (FoMaC) is to establish a community for developing theories, methods, and tools for dealing with the fact that change is not an exception, but the norm for today’s systems The initial issue of FoMaC comprises, in particular, contributions by the members of the editorial board, in order to indicate its envisioned style and range of papers, which cross-cuts various traditional research directions, but is characterized by its clear focus on change August 2016 Bernhard Steffen Organization Editor-in-Chief Bernhard Steffen TU Dortmund University, Germany Editorial Board Michael Felderer Klaus Havelund Mike Hinchey Reiner Hähnle Axel Legay Tiziana Margaria Arend Rensink Bernhard Steffen Stavros Tripakis Martin Wirsing University of Innsbruck, Austria Jet Propulsion Laboratory/NASA, USA Lero, Ireland TU Darmstadt, Germany Inria, France Lero, Ireland University of Twente, The Netherlands TU Dortmund University, Germany Aalto University and University of California, Berkeley, USA LMU, Munich, Germany LNCS Transaction on the Foundations for Mastering Change (FoMaC) Aims and Scope Everything Moves: Change Is No Exception for Today’s Systems — It Is the Norm The LNCS Transactions on Foundations for Mastering Change (FoMaC) intend to establish a forum for foundational research that fosters a discipline for rigorously dealing with the phenomenon of change In particular it addresses the very nature of today’s agile system development, which is characterized by unclear premises, unforeseen change, and the need for fast reaction, in a context of hard-to-control frame conditions, such as third-party components, network problems, and attacks We envision focused contributions that reflect and enhance the state of the art under the perspective of change This may comprise new theoretical results, analysis technology, tool support, experience reports and case studies, as well as pragmatics for change, i.e., user-centric approaches that make inevitable changes controllable in practice Papers may well focus on individual techniques, but must clearly position themselves in the FoMaC landscape Scope FoMaC is concerned with the foundations for mastering change and variation during the whole systems lifecycle at various conceptual levels, in particular during Meta-Modeling: This can be regarded as a technology transfer issue, where methods are considered to systematically adapt solutions from one (application) domain for another domain This comprises meta-modeling, generation of and transformations between domain-specific languages, as well as other issues of domain modeling and validation Modeling and Design: This is the main level at which “classic” variability modeling operates The methods considered here generalize classic modeling to specifically address variability issues, e.g., where and how to change things, and technology to maintain structural and semantical properties within the range of modeled variability Here methods such as feature modeling, “150 % modeling,” productline management, model-to-model transformations, constraint-based (requirement) specification, synthesis-based model completion, model checking, and feature interaction detection are considered Implementation: At this level, FoMaC addresses methods beyond classic parametric and modular programming approaches, such as aspect orientation, delta programming, program generation, generative programming, and program transformation, but X (FoMaC) Aims and Scope also static and dynamic validation techniques, e.g., program verification, symbolic execution, runtime verification, (model-based) testing, and test-based modeling, Runtime: This is the level of self-X technology, where methods are addressed that allow, steer, and control the autonomous evolution of systems during runtime These methods comprise techniques to achieve fault tolerance, runtime planning and synthesis, higher-order exchange of functionality, hot deployment and fail-over, and they should go hand in hand with the aforementioned dynamic validation techniques, such as program verification, symbolic execution, runtime verification, (model-based) testing, test-based modeling, and monitoring Evolution/Migration: This level is concerned with the long-term perspective of system evolution, i.e., the part where the bulk of costs is accumulated Central issues here are the change of platform, the merging of systems of overlapping functionality, the maintenance of downward compatibility, and the support of a continuous (system) improvement process, as well as continuous quality assurance, comprising regression testing, monitoring, delta testing, and model-based diagnostic features FoMaC comprises regular papers and Special Sections Both need to clearly focus on change Special Sections, however, provide the unique opportunity to shed light on a wider thematic context while establishing appropriate (change-oriented) links between the subtopics Submission of Manuscripts More detailed information, in particular concerning the submission process as well as a direct access to the editorial system, can be found under http://www.fomac.de/ Contents Introduction to the First Issue of FoMaC Bernhard Steffen Knowledge Management for Inclusive System Evolution Tiziana Margaria Archimedean Points: The Essence for Mastering Change Bernhard Steffen and Stefan Naujokat 22 Model Patterns: The Quest for the Right Level of Abstraction Arend Rensink 47 Verified Change Klaus Havelund and Rahul Kumar 71 Good Change and Bad Change: An Analysis Perspective on Software Evolution Mikael Lindvall, Martin Becker, Vasil Tenev, Slawomir Duszynski, and Mike Hinchey Compositional Model-Based System Design and Other Foundations for Mastering Change Stavros Tripakis Proof Repositories for Compositional Verification of Evolving Software Systems: Managing Change When Proving Software Correct Richard Bubel, Ferruccio Damiani, Reiner Hähnle, Einar Broch Johnsen, Olaf Owe, Ina Schaefer, and Ingrid Chieh Yu Statistical Model Checking with Change Detection Axel Legay and Louis-Marie Traonouez Collective Autonomic Systems: Towards Engineering Principles and Their Foundations Lenz Belzner, Matthias Hölzl, Nora Koch, and Martin Wirsing 90 113 130 157 180 Continuous Collaboration for Changing Environments Matthias Hölzl and Thomas Gabor 201 Issues on Software Quality Models for Mastering Change Michael Felderer 225 ... information corresponding to different concerns enrich the basic behavioural (functional) description, and are accessible as interpreted or non-interpreted information to the plugins Atomic propositions... practice as much care and foresight as optimal parents for the delivery itself and its acclimation in the operational environment This phase is called installation or implementation, depending on. .. Information like execution duration, costs, etc Knowledge Management for Inclusive System Evolution 17 are accessible to the interpreter or to various simulation environments Compatibility information

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