Nell Dale - C++ Plus Data Structures, 6th Edition
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Plus Data Structures
SIXTH EDITION
University of Texas, Austin
University of Massachusetts, Amherst
University of Massachusetts, Amherst
LEARNING
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Copyright 2018 by Jones & Bartlett Learning, LLC, an Ascend Learning Company
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Library of Congress Cataloging-in-Publication Data
Names: Dale, Nell (Nell B.), author. | Weems, Chip, author. | Richards, Tim
(Computer scientist), author.
Title: C++ plus data structures / Nell Dale, Chip Weems, Tim Richards, UMass
Amherst, Dept of Computer Science, MA.
Description: Sixth edition. | Burlington, Massachusetts : Jones & Bartlett
Learning, [2017] | Includes bibliographical references and index.
Identifiers: LCCN 2016009602 | ISBN 9781284089189 (casebound)
Subjects: LCSH: C++ (Computer program language) | Data structures (Computer science)
Classification: LCC QA76.73.C153 D334 2017 | DDC 005.13/3--dc23 LC record available at http://lccn.loc.gov/2016009602
6048
Printed in the United States of America
20 19 18 17 16 10 9 8 7 6 5 4 3 2 1
With this edition, two new authors come on board to carry forward the tradition of excellence in C ++ Plus Data Structures, as Nell Dale steps back from leading its future development. Chip Weems has been Nells coauthor on numerous other titles for 30 years, including Java Plus Data Structures, and contributed significantly to the pedagogical approach that set the tone for earlier versions of this text and its predecessors. Tim Richards was heavily involved with Chip and Nell in developing the most recent edition of Progamming and Problem Solving in C ++ . Together, they share a strong commitment to the success of students everywhere, which is, of course, the foundation for the love of teaching that motivates us all to walk into our classrooms each day.
Over the last two decades, the traditional data structures course has grown to include the broader topics of Abstract Data Types (ADTs), software engineering, and elementary analysis of algorithms.
The term data structures refers to the study of how to represent collections of data in organized relationships and how to write algorithms that manipulate them. The term Abstract Data Type (ADT) refers to a description of data in terms of a set of defining properties, as well as the operations that can be applied to the data. The shift in emphasis is representative of the move toward more abstraction in computer science education. We now study the abstract properties of classes of data objects, in addition to how the objects might be represented in a program. Johannes J. Martin puts it very succinctly: [D]epending on the point of view, a data object is characterized by its type (for the user) or by its structure (for the implementor).
The design of the abstraction and the implementation are both tied critically to software engineering, which seeks to apply engineering methodologies to the development of reliable, robust, and correct software. A poor abstraction can lead to a cumbersome set of use cases that force application programmers to either write unnecessarily complex code or neglect important validity checks. A poor implementation can be inefficient or prone to error. One aspect of designing an efficient implementation is being able to analyze the work done by a given algorithm. Thus, throughout this text, we distinguish between the engineering of abstractions and implementations as motivated by their applications, and we take the time to analyze the algorithms that we introduce.
The focus of C ++ Plus Data Structures is on ADTs as viewed from three different perspectives: their specification, their application, and their implementation. The specification describes the logical or abstract level and is concerned with what the data type represents. The application level is concerned with the use of the data in solving problems. This level is concerned with why the data type has particular properties and operations. The implementation level is where the operations are actually coded; it is concerned with the how questions.
Within this focus, we stress computer science theory and software engineering principles, including modularization, data encapsulation, information hiding, data abstraction, object-oriented decomposition, functional decomposition, the analysis of algorithms, and life-cycle software verification methods. We feel strongly that these principles should be introduced to computer science students early in their education so they learn to practice good software techniques from the beginning.
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