{"product_id":"9781119255918","title":"SAS Data Analytic Development: Dimensions of Software Quality","description":"\u003cp\u003e\u003cb\u003eDesign quality SAS software and evaluate SAS software quality\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSAS Data Analytic Development\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eA common fault in many software development environments is a focus on functional requirements—the \u003ci\u003ewhat\u003c\/i\u003e and \u003ci\u003ehow\u003c\/i\u003e—to the detriment of performance requirements, which specify instead \u003ci\u003ehow well\u003c\/i\u003e software should function (assessed through software execution) or \u003ci\u003ehow easily\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eAs 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.\u003c\/p\u003e 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, \u003ci\u003eSAS Data Analytic Development\u003c\/i\u003e recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements.","brand":"Wiley, John \u0026 Sons, Incorporated","offers":[{"title":"Default Title","offer_id":47107342336240,"sku":"9781119255918","price":75.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781119255918_p0.jpg?v=1763696951","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781119255918","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}