Chris Davis, Nike
The process of testing code ends up generating a lot of data, but rarely does this data come together in a consistent and insightful way. Normally teams need to focus on making sure immediate deliverables are high quality and they don’t have time to figure out metrics by distilling multiple sources. To truly make this data fit into today’s agile world the ability to make insightful data the product of a team’s work is crucial.
Quality Data Management (QDM) is the art of bringing insights and metrics from data throughout the Quality Management (QM) process back to scrum teams & business owners to show the true picture of quality for the entire Software Development Life Cycle (SDLC). Data points include task management, Kanban/Scrum boards, static code analysis, and automated functional test runs. Making sense of this diverse set of data ends up brining unstructured data analysis back to the QM team.
This session will be to discuss the opportunity of QDM, how to associate these disparate data points, and putting it all together in your automation pipeline.
Target audience: Intermediate
Chris Davis, 2014 Technical Paper, Paper
