Know-How in Programming Tasks: From Textual Tutorials to Task-Oriented Knowledge Graph

Accomplishing a program task usually involves performing multiple activities in a logical order. Task-solving activities may have different relationships, such as subactivityof, precede-follow, and different attributes, such as location, condition, API, code. We refer to task-solving activities and their relationships and attributes as know-how knowledge. Programming task know-how knowledge is commonly documented in semi-structured textual tutorials. A formative study of the 20 top-viewed Android-tagged how-to questions on Stack Overflow suggests that developers are faced with three information barriers (incoherent modeling of task intent, tutorial information overload and unstructured task activity description) for effectively discovering and understanding task-solving knowledge in textual tutorials. Knowledge graph has been shown to be effective in representing relational knowledge and supporting knowledge search in a structured way. Unfortunately, existing knowledge graphs extract only know-what information (e.g., APIs, API caveats and API dependencies) from software documentation. In this paper, we devise open information extraction (OpenIE) techniques to extract candidates for task activities, activity attributes and activity relationships from programming task tutorials. The resulting knowledge graph, TaskKG, includes a hierarchical taxonomy of activities, three types of activities relationships and five types of activity attributes, and enables activity-centric knowledge search. As a proof-of-concept, we apply our approach to Android Developer Guide. A comprehensive evaluation of TaskKG shows high accuracy of our OpenIE techniques. A user study shows that TaskKG is promising in helping developers finding correct answers to programming how-to questions.