Books for the Business Analyst

Scientifically Define Software Requirements

Scientifically Define Software Requirements
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Categories: Requirements

Author: Jerry Zhu Ph.D.

Requirements errors consume 25% to 40% of the total project budget according to Carnegie Mellon University (CMU). Requirements errors discovered after coding introduce more rounds of reanalyzing, redesigning, recoding and retesting. Worse, attempts to fix a requirements error often introduce new ones. If too many errors are produced, the cost and time needed to complete the system become so great that going on does not make sense. These requirements errors, however, are avoidable. “We waste billions of dollars each year on entirely preventable mistakes,” according to an IEEE Spectrum article.

The increasing new stream of software development processes may solve some problems of its predecessors but inevitably has also introduced new problems. These practical problems are reducible to a small number of theoretical problems. As the theoretical problems are indentified and solved, scientific theories are created. The scientific theories are then incorporated in real world affairs to form applied methodologies that, in turn, solve all known practical problems.

This e-book is a collection of five white papers and two published papers. The book introduces the theoretical problem of requirements engineering, the theory to solve the problem, and the resulting scientific approach. It shows why it is possible to avoid most if not all costly requirements errors and steps to do so.

Edwards Deming suggested that improved quality leads to cost decrease because of less rework, fewer mistakes, fewer delays, snags; better use of machine-time and materials. This book helps maximizing the requirements quality so that the number of iterations is minimized, customer satisfaction is maximized, and the capability to develop large systems is dramatically increased. It also helps creating a common frame of thinking and a shared worldview as the basis for making decisions to avoid entangling in constant arguments and failure to deal with underlying assumptions.


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