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SAS Datenanalytisc
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- Sofort-KaufenSAS Datenanalytische Entwicklung: Dimensionen der Softwarequalität, Troy Hughes
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eBay-Artikelnr.:225716123404
Artikelmerkmale
- Artikelzustand
- Artist
- Hughes, Troy Martin
- Brand
- N/A
- Date of Publication
- 20161021
- EAN
- 9781119240761
- ISBN
- 111924076X
- Book Title
- SAS Data Analytic Development: Dimensions of Software Quality ...
- Release Title
- SAS Data Analytic Development: Dimensions of Software Quality ...
- Colour
- N/A
Über dieses Produkt
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
111924076X
ISBN-13
9781119240761
eBay Product ID (ePID)
5038456442
Product Key Features
Number of Pages
624 Pages
Language
English
Publication Name
Sas Data Analytic Development : Dimensions of Software Quality
Subject
Programming / General, Mathematical & Statistical Software
Publication Year
2016
Type
Textbook
Subject Area
Computers
Series
Wiley and Sas Business Ser.
Format
Hardcover
Dimensions
Item Height
1.9 in
Item Weight
36.9 Oz
Item Length
9.4 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2016-021300
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
005.5/5
Table Of Content
Preface xi Acknowledgments xvi About the Author xvii Chapter 1 Introduction 1 Distinguishing Data Analytic Development 3 Software Development Life Cycle (SDLC) 7 Risk 14 Chapter 2 Quality 21 Defining Quality 24 Software Product Quality Model 30 Quality in the SDLC 40 Chapter 3 Communication 49 Return Codes 51 System Numeric Return Codes 53 System Alphanumeric Return Codes 70 User-Generated Return Codes 74 Parallel Processing Communication 79 PART I DYNAMIC PERFORMANCE 85 Chapter 4 Reliability 87 Defining Reliability 90 Paths to Failure 91 ACL: The Reliability Triad 102 Reliability in the SDLC 108 Chapter 5 Recoverability 123 Defining Recoverability 125 Recoverability toward Reliability 127 Recoverability Matrix 131 TEACH Recoverability Principles 132 SPICIER Recoverability Steps 136 Recovering with Checkpoints 148 Recoverability in the SDLC 151 Chapter 6 Robustness 159 Defining Robustness 162 Robustness toward Reliability 163 Defensive Programming 164 Exception Handling 172 Robustness in the SDLC 203 Chapter 7 Execution Efficiency 207 Defining Execution Efficiency 209 Factors Affecting Execution Efficiency 210 False Dependencies 211 Parallel Processing 220 Execution Efficiency in the SDLC 232 Chapter 8 Efficiency 243 Defining Efficiency 246 Disambiguating Efficiency 246 Defining Resources 249 Efficiency in the SDLC 259 Chapter 9 Scalability 273 Defining Scalability 276 The Scalability Triad 276 Resource Scalability 278 Demand Scalability 279 Load Scalability 290 Scalability in the SDLC 309 Chapter 10 Portability 313 Defining Portability 316 Disambiguating Portability 317 3GL versus 4GL Portability 318 Facets of Portability 319 Portability in the SDLC 338 Chapter 11 Security 341 Defining Security 344 Confidentiality 344 Integrity 345 Availability 365 Security in the SDLC 379 Chapter 12 Automation 383 Defining Automation 386 Automation in SAS Software 387 SAS Processing Modes 388 Starting in Interactive Mode 393 Starting in Batch Mode 410 Automation in the SDLC 415 PART II STATIC PERFORMANCE 419 Chapter 13 Maintainability 421 Defining Maintainability 424 Maintenance 425 Maintenance in the SDLC 429 Failure to Maintain 436 Maintainability 440 Chapter 14 Modularity 447 Defining Modularity 449 From Monolithic to Modular 450 Modularity Principles 454 Benefits of Modularity 474 Chapter 15 Readability 477 Defining Readability 479 Plan to Get Hit by a Bus 480 Software Readability 481 External Readability 503 Chapter 16 Testability 507 Defining Testability 510 Software Testing 510 Testability 538 Chapter 17 Stability 541 Defining Stability 543 Achieving Stability 544 Stable Requirements 545 Defect-Free Code 546 Dynamic Flexibility 546 Stability and Beyond 549 Modularizing More Than Macros 559 Chapter 18 Reusability 577 Defining Reusability 579 Reuse 580 Reusability 588 From Reusability to Extensibility 597 Index 603
Synopsis
Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer's compendium for writing better-performing software and the manager's guide to building comprehensive software performance requirements. The text introduces and parallels the International Organization for Standardization (ISO) software product quality model, demonstrating 15 performance requirements that represent dimensions of software quality, including: reliability, recoverability, robustness, execution efficiency (i.e., speed), efficiency, scalability, portability, security, automation, maintainability, modularity, readability, testability, stability, and reusability. The text is intended to be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault in many software development environments is a focus on functional requirements--the what and how --to the detriment of performance requirements, which specify instead how well software should function (assessed through software execution) or how easily software should be maintained (assessed through code inspection). Without the definition and communication of performance requirements, developers risk either building software that lacks intended quality or wasting time delivering software that exceeds performance objectives--thus, either underperforming or gold-plating, both of which are undesirable. Managers, customers, and other decision makers should also understand the dimensions of software quality both to define performance requirements at project outset as well as to evaluate whether those objectives were met at software completion. As data analytic software, SAS transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS literature; however, code quality is far less commonly described and too often references only the speed or efficiency with which software should execute, omitting other critical dimensions of software quality. SAS® software project definitions and technical requirements often fall victim to this paradox, in which rigorous quality requirements exist for data and data products yet not for the software that undergirds them. By demonstrating the cost and benefits of software quality inclusion and the risk of software quality exclusion, stakeholders learn to value, prioritize, implement, and evaluate dimensions of software quality within risk management and project management frameworks of the software development life cycle (SDLC). Thus, SAS Data Analytic Development recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements., Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer s compendium for writing better-performing software and the manager s guide to building comprehensive software performance requirements., Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer's compendium for writing better-performing software and the manager's guide to building comprehensive software performance requirements. The text introduces and parallels the International Organization for Standardization (ISO) software product quality model, demonstrating 15 performance requirements that represent dimensions of software quality, including: reliability, recoverability, robustness, execution efficiency (i.e., speed), efficiency, scalability, portability, security, automation, maintainability, modularity, readability, testability, stability, and reusability. The text is intended to be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault in many software development environments is a focus on functional requirements--the what and how --to the detriment of performance requirements, which specify instead how well software should function (assessed through software execution) or how easily software should be maintained (assessed through code inspection). Without the definition and communication of performance requirements, developers risk either building software that lacks intended quality or wasting time delivering software that exceeds performance objectives--thus, either underperforming or gold-plating, both of which are undesirable. Managers, customers, and other decision makers should also understand the dimensions of software quality both to define performance requirements at project outset as well as to evaluate whether those objectives were met at software completion. As data analytic software, SAS transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS literature; however, code quality is far less commonly described and too often references only the speed or efficiency with which software should execute, omitting other critical dimensions of software quality. SAS(R) software project definitions and technical requirements often fall victim to this paradox, in which rigorous quality requirements exist for data and data products yet not for the software that undergirds them. By demonstrating the cost and benefits of software quality inclusion and the risk of software quality exclusion, stakeholders learn to value, prioritize, implement, and evaluate dimensions of software quality within risk management and project management frameworks of the software development life cycle (SDLC). Thus, SAS Data Analytic Development recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements., Praise for SAS DATA ANALYTIC DEVELOPMENT "The quality of your programs is often reflected in the quality of your data, and your programs will be substantially improved by implementing the concepts presented in this book. All programmers, even non-SAS programmers, who write programs that work with data will find this book both highly useful and interesting, especially so if you have ever backpacked." Art Carpenter, California Occidental Consultants "Troy's fantastic book will greatly increase the ability of the reader to positively impact their organization. He raises the capabilities and maturity of the SAS community by sharing his many years of software development experience with us in a style that delights and clearly informs. Thank you, Troy!" Chuck Kincaid, Senior Engagement Director, Experis Business Analytics "Troy's book has prompted a paradigm shift in my own work: I realize that the SAS programs I write are deserving of the same rigorous quality controls, and the same steps to ensure quality, as all software products. I heartily recommend this book to SAS practitioners (and their managers) involved in data analytic development those who 'produce the golden egg the valuable data product.'" Louise Hadden, Lead Programmer Analyst, U.S. Health Division, Abt Associates "Concepts are well defined, usually starting with a general statement and followed up with a technical definition (for example, from ISO documentation). I think all experienced SAS programmers can benefit from the book, and it is a must-read for all new SAS programmers." Peter Eberhardt, Consultant, Fernwood Consulting Group Inc. "An absolute must-read for data analysts to remind them that software programming is a very central component of their job. Concepts, best practices, and SAS code examples perfectly illustrate this increasingly important topic." Gerhard Svolba, Ph.D., Data Scientist, SAS Institute
LC Classification Number
QA276.45.S27H84 2016
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