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What should be done when a feature hypothesis has not been satisfied?

  1. Move the feature to the done column

  2. Continue work until the hypothesis is satisfied

  3. Discard the feature entirely

  4. Document the effort without further action

The correct answer is: Continue work until the hypothesis is satisfied

When a feature hypothesis has not been satisfied, it's essential to continue work until the hypothesis is satisfied. This approach is rooted in the principles of Lean and Agile methodologies, which emphasize the importance of validating assumptions and learning from experiments. Continuing to work on the feature allows teams to explore alternative solutions, make adjustments based on feedback, and gather additional data to understand why the hypothesis was not met. In this context, a hypothesis is essentially an assumption about how a particular feature will perform or how users will respond to it. If the hypothesis is not satisfied, it presents an opportunity for the team to delve deeper, engage with stakeholders, and iterate on the feature. This iterative approach aligns with the core values of DevOps, which prioritize delivering value and adapting based on real-world performance rather than prematurely discarding work that may still offer potential benefits. The other choices do not align with the principle of continuous improvement and validation of features. Moving the feature to the done column suggests that the work is complete, which is misleading if the hypothesis remains untested. Discarding the feature entirely would preemptively eliminate potential value before proper evaluation. Documenting the effort without further action lacks an active response and does not support the iterative process necessary for refining features based on empirical evidence