Minimum Viable Hypothesis

Requirements Management Blogs

In this blog post, James Shore suggests that the concept Minimum Viable Hypothesis should replace the idea of Minimum Viable Product (MVP). His point is that when you focus on the product you could end up being in love with it.

With the Minimum Viable Hypothesis approach, the first step after defining the problem to solve is not to create a minimum viable product, but rather to brainstorm market hypotheses. The important question is “Which groups have the desire and funds for a solution?” You use the build-measure-learn cycle to validate the riskiest assumptions about your market. At the end of the build-measure-learn cycle, you have make the decision to pivot to a new market or persevere. The product is built as a last resort.

software requirements group
Articles Knowledge

Reviewing Requirements for Testability

Modern software development approaches like Agile and Scrum support a strong collaboration between all member of the software development team, software testers and business analysts included. Even if you don’t use a method like Behavior-Driven Development (BDD) or Specification by Example, checking the fact that you will be able to actually test your requirements is […]

Read More
Requirements Management Articles
Articles Knowledge

User Stories for Both Requirements and Testing

User stories are a technique taken from the agile development playbook that can easily be applied in traditional systems development and maintenance. User stories help you document needs in a structured way, from the users’ perspective. They’re a good basis for test cases, so as to support integrated requirements management and testing. In this article, […]

Read More
Requirements Management Articles
Articles Knowledge

Understanding System Analysis Models

This article is an extract of the “Complete Systems Analysis” written by James and Suzanne Robertson. It explains the basics of analysis models and emphasize that the important thing to remember is that modeling tools are complementary. Each shows one aspect of the system. Together, they make a complete working model of the system.

Read More

Copyright © 2009-2021 Martinig & Associates