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This paper targets the problem of speech act detection in conversations aboutbug repair. We conduct a "Wizard of Oz" experiment with 30 professionalprogrammers, in which the programmers fix bugs for two hours, and use asimulated virtual assistant for help. Then, we use an open coding manualannotation procedure to identify the speech act types in the conversations.Finally, we train and evaluate a supervised learning algorithm to automaticallydetect the speech act types in the conversations. In 30 two-hour conversations,we made 2459 annotations and uncovered 26 speech act types. Our automateddetection achieved 69% precision and 50% recall. The key application of thiswork is to advance the state of the art for virtual assistants in softwareengineering. Virtual assistant technology is growing rapidly, thoughapplications in software engineering are behind those in other areas, largelydue to a lack of relevant data and experiments. This paper targets this problemin the area of developer Q/A conversations about bug repair.

Detecting Speech Act Types in Developer Question/Answer Conversations During Bug Repair