(Illustrated above) A scientist writes her own algorithm within her analysis application to visualize the data from one of her lab’s clinical research studies. She starts with a standard algorithm provided by the tool, then exploits defined flexibilities to modify its rules toward the new visualization approach that she wants to try.

A financial trader is reviewing performance trends in his group in order to find potential areas for improvement. To locate only deals made within the last month that match highly specific criteria, he uses his trading application to compose and save a long and complex Boolean query.

An architect selects an option to have her building modeling application record her actions. Once she has finished recording the interaction sequence, she can then easily apply the same sequence of steps to other objects within the same project.

What do targeted individuals and organizations think of any end user programming features in their current computing tools?

What programmed extensions have they created using these features, and who created them?

Which of the work practices that your team is striving to mediate contains important variabilities in rule based processes?

What types of changes might targeted knowledge workers want make to the default algorithms in your sketched application concepts? Are these changes predictable enough to become customization options — or are they diverse yet important enough to warrant some kind of programming flexibility?

Might programming needs be so infrequent and unchanging as to make technical openness to extension by IT staff more desirable than end user programming options?

What larger technology trends and advanced analogies to other products could valuably inform your team’s envisioning of potential programming concepts?

What interactive programming methods could allow users to flexibly “code” algorithms in ways that are appropriate for their skills and the targeted domain context?

How might your sketched functionality ideas for end user programming allow individuals to carve out particular yet easily understandable paths through well characterized spaces of rule based possibility?

How might people test out their own algorithms without any risks to valued data? How could these testing outputs reference your product’s larger error prevention and handling conventions?

How might your team eventually learn from the changes that your users will presumably make via programming options in your computing tool? How could your product’s overall system promote community sharing of these local innovations?

Do you have enough information to usefully answer these and other envisioning questions? What additional research, problem space models, and design concepting could valuably inform your team’s application envisioning efforts?