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Automatically recommending API-related tutorial fragments or Q&A pairs from Stack Overflow (SO) is very helpful for developers, especially when they need to use unfamiliar APIs to complete programming tasks. However, in practice developers are more likely to express the API-related questions using natural language when they do not know the exact name of an unfamiliar API. In this paper, we propose an approach, called SOTU, to automatically find answers for API-related natural language questions (NLQs) from tutorials and SO. We first identify relevant API-related tutorial fragments and extract API-related Q&A pairs from SO. We then construct an API-Answer corpus by combining these two sources of information. For an API-related NLQ given by the developer, we parse it into several potential APIs and then retrieve potential answers from the API-Answer corpus. Finally, we return a list of potential results ranked by their relevancy. Experiments on API-Answer corpus demonstrate the effectiveness of SOTU.
Wu et al. (Sun,) studied this question.
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