Object Oriented Analysis And Design With Applications 3Rd Edition
Bibliographic record
Abstract
Object-Oriented Design with Applications has long been the essential reference to object-oriented technology, which, in turn, has evolved to join the mainstream of industrial-strength software development. In this third edition--the first revision in 13 years--readers can learn to apply object-oriented methods using new paradigms such as Java, the Unified Modeling Language (UML) 2.0, and .NET.The authors draw upon their rich and varied experience to offer improved methods for object development and numerous examples that tackle the complex problems faced by software engineers, including systems architecture, data acquisition, cryptoanalysis, control systems, and Web development. They illustrate essential concepts, explain the method, and show successful applications in a variety of fields. You'll also find pragmatic advice on a host of issues, including classification, implementation strategies, and cost-effective project management.New to this new edition are An introduction to the new UML 2.0, from the notation's most fundamental and advanced elements with an emphasis on key changes New domains and contexts A greatly enhanced focus on modeling--as eagerly requested by readers--with five chapters that each delve into one phase of the overall development lifecycle. Fresh approaches to reasoning about complex systems An examination of the conceptual foundation of the widely misunderstood fundamental elements of the object model, such as abstraction, encapsulation, modularity, and hierarchy How to allocate the resources of a team of developers and mange the risks associated with developing complex software systems An appendix on object-oriented programming languagesThis is the seminal text for anyone who wishes to use object-oriented technology to manage the complexity inherent in many kinds of systems.Sidebarsi¾ i¾ Prefacei¾ Acknowledgments i¾ i¾ About the Authors i¾ i¾ Section I: Conceptsi¾ i¾ Chapter 1: Complexityi¾ i¾ i¾ Chapter 2: The Object Model i¾ i¾ Chapter 3: Classes and Objects i¾ i¾ Chapter 4: Classification i¾ i¾ Section II: Method i¾ Chapter 5: Notation i¾ i¾ Chapter 6: Process Chapter 7: Pragmaticsi¾ i¾ i¾ Chapter 8: System Architecture: Satellite-Based Navigation i¾ i¾ Chapter 9: Control System: Traffic Management i¾ i¾ Chapter 10: Artificial Intelligence: Cryptanalysis i¾ i¾ Chapter 11: Data Acquisition: Weather Monitoring Station i¾ Chapter 12: Web Application: Vacation Tracking System i¾ i¾ i¾ Appendix A: Object-Oriented Programming Languagesi¾ Appendix B: Further Reading i¾ i¾ Notes i¾ i¾ Glossary i¾ i¾ Classified Bibliography i¾ i¾ Index i¾ i¾
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.080 | 0.087 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".