An entire analysis curriculum built on the synthesis of quality management and software engineering principles.

The major intersection of quality and software occurs during requirements activities. This series of courses covers the elicitation and modeling of business, data, and process requirements with an emphasis on translating those requirements forward toward implementation. Note: This course series concentrates heavily on the front-end of the software lifecycle. Emphasis is placed on business and functional specification, architectural design, and testing strategies and plans. Technical aspects and nuances of building and integrating technology components is beyond the scope of this series.

Introduction to Requirements Engineering

This course ...

Quality-Based Requirements Analysis

Analysis improves the definition of requirements for information systems by better understanding the three types of requirements, how they conflict and interact, and how best to capture and record requirements results to minimize omissions and errors. (3 days)

Quality-Based Data Modeling

Data modeling improves the identification and analysis of data requirements at the enterprise, project, and data base levels. Through a synthesis of basic quality management principles, data model rigor is increased, resulting in the absence of data omissions and defects often encountered with traditional data analysis techniques. (3 days)

Quality-Based Process Modeling

Process modeling improves the thoroughness of business process definitions through a rigorous adaptation of concepts inherited from basic quality management practices. Using principles of customer-supplier and requirement-conformance feedback loops, process models create business definitions that can be easily verified and that reveal scope issues that normally create project problems during the implementation phase. (3 days)

Quality-Based Model Integration

Integration improves the verification process of assuring that all analysis models can be properly integrated into a single whole. Even a small project is likely to develop dozens, if not hundreds, of analysis process and data models in support of its requirements. Even if every model is correct, there still is no assurance that all of the models will fit together properly. Proper integration requires active steps on the part of the analyst to assure project success. (2 days)

Quality-Based Design Transition

Transition improves the activities required to move analysis deliverables on to system design. Many organizations that invest heavily in analysis modeling still lose much of that investment by setting aside the models at the beginning of design to revert to traditional design techniques. Through proper transition techniques, system and data architectures can be derived from the analysis models, maximizing the return on investment in modeling. (2 days)