ARSeek: identifying API resource using code and discussion on stack overflow

• Main Authors: LUONG, Gia Kien, HADI, Mohammad, Ferdian, Thung, FARD, Fatemeh H., LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: API resource; API embedding; Content classification; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7692; https://ink.library.smu.edu.sg/context/sis_research/article/8695/viewcontent/929800a331.pdf
• Institution: Singapore Management University

It is not a trivial problem to collect API-relevant examples, usages, and mentions on venues such as Stack Overflow. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the Stack Overflow thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop ARSeek, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a discussion. ARSeek combines a syntactic word-based score with a score from a predictive model fine-tuned from CodeBERT. In terms of F1-score, ARSeek achieves an average score of 0.8709 and beats the state-of-the-art approach by 14%.