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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.
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