SAS Data Analytic Development: Dimensions of Software Quality (Wiley and SAS Business Series)

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.

設計高品質的SAS軟體並評估SAS軟體的品質

《SAS資料分析開發》是開發人員撰寫效能更佳軟體的手冊，也是管理者建立全面軟體效能需求的指南。本書引入並與國際標準化組織（ISO）軟體產品品質模型相呼應，展示了15個代表軟體品質維度的效能需求，包括：可靠性、可恢復性、健壯性、執行效率（即速度）、效能、可擴展性、可移植性、安全性、自動化、可維護性、模組化、可讀性、可測試性、穩定性和可重用性。本書旨在全面閱讀或作為參考工具，以指導、激勵、提供和評估軟體品質。

許多軟體開發環境中常見的錯誤是過度關注功能需求（即「什麼」和「如何」），而忽略了性能需求，後者則指定軟體應該如何運作（通過軟體執行評估）或軟體應該如何容易維護（通過程式碼檢查評估）。如果沒有定義和傳達性能需求，開發人員可能會建立缺乏預期品質的軟體，或浪費時間交付超出性能目標的軟體，從而出現性能不佳或過度裝飾的情況，這兩種情況都是不可取的。管理者、客戶和其他決策者也應該了解軟體品質的維度，以便在項目開始時定義性能需求，並評估軟體完成時是否達到了這些目標。

作為資料分析軟體，SAS將數據轉化為信息，最終形成知識和基於數據的決策。不出所料，數據品質是SAS文獻的核心關注和主題；然而，程式碼品質很少被描述，且往往只提到軟體應該執行的速度或效率，忽略了其他關鍵的軟體品質維度。SAS軟體項目的定義和技術需求往往陷入這種矛盾，即對數據和數據產品存在嚴格的品質要求，但對支撐它們的軟體卻沒有。

通過展示軟體品質包含的成本和效益，以及軟體品質排除的風險，利益相關者學會在軟體開發生命週期（SDLC）的風險管理和項目管理框架中重視、優先、實施和評估軟體品質的維度。因此，《SAS資料分析開發》重新調整了商業價值，將程式碼品質與數據品質並列，將性能需求與功能需求並列。