Read online book Wiley and SAS Business: SAS Data Analytic Development : Dimensions of Software Quality DJV, EPUB, PDF
9781119240761 111924076X Design quality SAS software and evaluate SAS software quality "SAS(R) 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(R) transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS(R) 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(R) 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. 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. 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 software and evaluate software quality to deliver analytic solutions SAS Data Analytic Development is the developer's compendium for writing better-performing code and the manager's guide to building comprehensive software performance requirements. Chapters explore a Base SAS implementation of dynamic performance requirements (i.e. code reliability, robustness, efficiency, scalability, portability, and recoverability) and static performance requirements (i.e. code maintainability, modularity, extensibility, stability, reusability, and readability) and are presented in an example-based framework that can be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault of software development is its focus on functional requirements-the what and how -to the detriment of performance requirements (once called non-functional requirements). Dynamic performance requirements specify instead how well software should function and are assessed through software execution, while static performance requirements specify how easily software should be maintained and are 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 also should 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, the SAS suite 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 and 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 same paradox, in which attention overwhelmingly is paid to data and data products yet not to the code that undergirds them. Because software quality can only be achieved through both functional and performance requirements, SAS Data Analytic Development recalibrates this goal with individual chapters that address the benefits, nomenclature, technical implementation, and supporting case studies of 12 performance requirements. Moreover, because increased performance is inherently balanced against potentially increased costs, increased development time, and decreased functionality, the content allows developers to build processes that efficiently target their efforts, delivering high quality only where stakeholders place value., This book is intended to be industry-agnostic and will feature examples from a broad spectrum of industries and topics with the intent of expressing relevancy to as many readers as possible. In the introductory chapter that discusses quality, the author discusses aspects of code quality that can be instilled at the industry or organizational levels. One example will be mention of FDA regulations that positively influence certain aspects of code quality. The intent is to demonstrate that an industry or organization may require or facilitate certain attributes of code quality (or, in some cases, perpetuate bad habits or practice) and that a more holistic view of quality can be gained by surveying practices across several industries and organizations. Other examples of how specific industries can positively influence coding practices will include the use of SAS in research, financial institutions, emergency medicine, and national security. The book will be example-driven (using Base SAS) with a few chapters that point theoretically to other uses of the concepts and examples presented. Most chapters will introduce a dimension of quality (i.e., either a dynamic or static performance attribute) and subsequently demonstrate several technical examples of how that attribute can be implemented. Because many performance requirements overlap or are closely related, many sections will point to other chapters for further understanding or other related examples. Each chapter will conclude with at least one comprehensive example that demonstrates the short business case that required various quality attributes and how those attributes were implemented. Case studies will depict successful and unsuccessful implementation of code quality against requirements. These cases will be more anecdotal from the author's personal experiences--good and bad--and thus will be neither code-driven nor theoretical.
9781119240761 111924076X Design quality SAS software and evaluate SAS software quality "SAS(R) 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(R) transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS(R) 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(R) 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. 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. 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 software and evaluate software quality to deliver analytic solutions SAS Data Analytic Development is the developer's compendium for writing better-performing code and the manager's guide to building comprehensive software performance requirements. Chapters explore a Base SAS implementation of dynamic performance requirements (i.e. code reliability, robustness, efficiency, scalability, portability, and recoverability) and static performance requirements (i.e. code maintainability, modularity, extensibility, stability, reusability, and readability) and are presented in an example-based framework that can be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault of software development is its focus on functional requirements-the what and how -to the detriment of performance requirements (once called non-functional requirements). Dynamic performance requirements specify instead how well software should function and are assessed through software execution, while static performance requirements specify how easily software should be maintained and are 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 also should 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, the SAS suite 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 and 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 same paradox, in which attention overwhelmingly is paid to data and data products yet not to the code that undergirds them. Because software quality can only be achieved through both functional and performance requirements, SAS Data Analytic Development recalibrates this goal with individual chapters that address the benefits, nomenclature, technical implementation, and supporting case studies of 12 performance requirements. Moreover, because increased performance is inherently balanced against potentially increased costs, increased development time, and decreased functionality, the content allows developers to build processes that efficiently target their efforts, delivering high quality only where stakeholders place value., This book is intended to be industry-agnostic and will feature examples from a broad spectrum of industries and topics with the intent of expressing relevancy to as many readers as possible. In the introductory chapter that discusses quality, the author discusses aspects of code quality that can be instilled at the industry or organizational levels. One example will be mention of FDA regulations that positively influence certain aspects of code quality. The intent is to demonstrate that an industry or organization may require or facilitate certain attributes of code quality (or, in some cases, perpetuate bad habits or practice) and that a more holistic view of quality can be gained by surveying practices across several industries and organizations. Other examples of how specific industries can positively influence coding practices will include the use of SAS in research, financial institutions, emergency medicine, and national security. The book will be example-driven (using Base SAS) with a few chapters that point theoretically to other uses of the concepts and examples presented. Most chapters will introduce a dimension of quality (i.e., either a dynamic or static performance attribute) and subsequently demonstrate several technical examples of how that attribute can be implemented. Because many performance requirements overlap or are closely related, many sections will point to other chapters for further understanding or other related examples. Each chapter will conclude with at least one comprehensive example that demonstrates the short business case that required various quality attributes and how those attributes were implemented. Case studies will depict successful and unsuccessful implementation of code quality against requirements. These cases will be more anecdotal from the author's personal experiences--good and bad--and thus will be neither code-driven nor theoretical.