INCOSE Guide to Writing Requirements v4.0

The cover of the new version of the INCOSE Guide to Writing Requirements that help users distinguish needs from requirements while writing or verifying them

At the crossroads between needs and requirement statements

Description:

This webinar will shed light on the major improvements to the INCOSE Guide to Writing Requirements, which has been a reference for many years for systems engineers willing to improve their needs & requirements writing.

Apart from the alignment with other guides produced by the INCOSE Requirements Working Group (such as the Needs and Requirements Manual) released in an intermediate v3.1, this version 4.0 of the guide has brought significant material to help users distinguish needs from requirements, either while writing or verifying the requirements.

The rules catalogue (Section 4 of the Guide) can be adapted to either need statements or requirements statements. Adopting a progressive and iterative approach in the verification of needs and requirements specifications has proven itself to be much more efficient than traditional review processes. It also ensures requirements writers focus more on the content than the syntax, which structure can be standardized with the use of patterns (also called boilerplates), as described in Appendix A.

This progressive approach has been summarized in a Rule Applicability Matrix (Appendix D), which enables the guide users to identify at a glance which rules they are recommended to apply, and recalls the importance of tailoring the set of rules to their specific context (industry, project-specific procedures, quality management policies, etc.).

To achieve this tailored set of rules, the matrix also helps assess which of the applied rules would require a project data dictionary to be defined.

The final objective of these improvements is to overcome the challenge that is set in Section 1.8 of the guide, which is to “address both need and requirement statements and understand the differences between the two”, by means of NLP-based assistants.