SAS Data Analytic Development: Dimensions of Software Quality

Troy Martin Hughes



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.

Table of Contents

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

下载地址：