Leveraging Official Content and Social Context to Recommend Software Documentation

For an unfamiliar Application Programming Interface (API), software developers often access the official documentation to learn its usage, and post questions related to this API on social question and answering (QA) sites to seek solutions. The official software documentation often captures the information about functionality and parameters, but lacks detailed descriptions in different usage scenarios. On the contrary, the discussions about APIs on social QA sites provide enriching usages. Moreover, existing code search engines and information retrieval systems cannot effectively return relevant software documentation when the issued query does not contain code snippets or API-like terms. In this paper, we present \mathsf{CnCxL2R}CnCxL2R, a software documentation recommendation strategy incorporating the content of official documentation and the social context on QA into a learning-to-rank schema. In the proposed strategy, the content, local context and global context of documentation are considered to select candidate documents. Then four types of features are extracted to learn a ranking model. We conduct a large-scale automatic evaluation on Java documentation recommendation. The results show that \mathsf{CnCxL2R}CnCxL2R achieves state-of-the-art performance over the eight baseline models. We also compare the \mathsf{CnCxL2R}CnCxL2R with Google search. The results show that \mathsf{CnCxL2R}CnCxL2R can recommend more relevant software documentation, and can effectively capture the semantic between the high-level intent in developers' queries and the low-level implementation in software documentation.