  Virtual Assistant technology is rapidly proliferating to improve productivity
in a variety of tasks. While several virtual assistants for everyday tasks are
well-known (e.g., Siri, Cortana, Alexa), assistants for specialty tasks such as
software engineering are rarer. One key reason software engineering assistants
are rare is that very few experimental datasets are available and suitable for
training the AI that is the bedrock of current virtual assistants. In this
paper, we present a set of Wizard of Oz experiments that we designed to build a
dataset for creating a virtual assistant. Our target is a hypothetical virtual
assistant for helping programmers use APIs. In our experiments, we recruited 30
professional programmers to complete programming tasks using two APIs. The
programmers interacted with a simulated virtual assistant for help - the
programmers were not aware that the assistant was actually operated by human
experts. We then annotated the dialogue acts in the corpus along four
dimensions: illocutionary intent, API information type(s), backward-facing
function, and traceability to specific API components. We observed a diverse
range of interactions that will facilitate the development of dialogue
strategies for virtual assistants for API usage.

Software Engineering;Human-Computer Interaction