{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:56:12Z","timestamp":1763664972622,"version":"3.41.0"},"reference-count":88,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>\n            When encountering unfamiliar APIs, developers tend to seek help from API tutorials and Stack Overflow (SO). API tutorials help developers understand the API knowledge in a general context, while SO often explains the API knowledge in a specific programming task. Thus, tutorials and SO posts together can provide more API knowledge. However, it is non-trivial to retrieve API knowledge from both API tutorials and SO posts based on natural language queries. Two major problems are irrelevant API knowledge in two different resources and the lexical gap between the queries and documents. In this article, we regard a fragment in tutorials and a Question and Answering (Q&amp;A) pair in SO as a\n            <jats:italic>knowledge item<\/jats:italic>\n            (KI). We generate \u27e8\n            <jats:italic>API, FRA<\/jats:italic>\n            \u27e9 pairs (FRA stands for fragment) from tutorial fragments and APIs and build \u27e8\n            <jats:italic>API, QA<\/jats:italic>\n            \u27e9 pairs based on heuristic rules of SO posts. We fuse \u27e8\n            <jats:italic>API, FRA<\/jats:italic>\n            \u27e9 pairs and \u27e8\n            <jats:italic>API, QA<\/jats:italic>\n            \u27e9 pairs to generate API knowledge (AK for short) datasets, where each data item is an \u27e8\n            <jats:italic>API, KI<\/jats:italic>\n            \u27e9 pair. We propose a novel approach, called PLAN, to automatically retrieve API knowledge from both API tutorials and SO posts based on natural language queries. PLAN contains three main stages: (1) API knowledge modeling, (2) query mapping, and (3) API knowledge retrieving. It first utilizes a deep-transfer-metric-learning-based relevance identification (DTML) model to effectively find relevant \u27e8\n            <jats:italic>API, KI<\/jats:italic>\n            \u27e9 pairs containing two different knowledge items (\u27e8\n            <jats:italic>API, QA<\/jats:italic>\n            \u27e9 pairs and \u27e8\n            <jats:italic>API, FRA<\/jats:italic>\n            \u27e9 pairs) simultaneously. Then, PLAN generates several potential APIs as a way to reduce the lexical gap between the query and \u27e8\n            <jats:italic>API, KI<\/jats:italic>\n            \u27e9 pairs. According to potential APIs, we can select relevant \u27e8\n            <jats:italic>API, KI<\/jats:italic>\n            \u27e9 pairs to generate potential results. Finally, PLAN returns a list of ranked \u27e8\n            <jats:italic>API, KI<\/jats:italic>\n            \u27e9 pairs that are related to the query. We evaluate the effectiveness of PLAN with 270 queries on Java and Android AK datasets containing 10,072 \u27e8\n            <jats:italic>API, KI<\/jats:italic>\n            \u27e9 pairs. Our experimental results show that PLAN is effective and outperforms the state-of-the-art approaches. Our user study further confirms the effectiveness of PLAN in locating useful API knowledge.\n          <\/jats:p>","DOI":"10.1145\/3565799","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T13:19:53Z","timestamp":1665148793000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Retrieving API Knowledge from Tutorials and Stack Overflow Based on Natural Language Queries"],"prefix":"10.1145","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1096-7074","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, China and the State Key Laboratory for Novel Software Technology, Nanjing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0392-8475","authenticated-orcid":false,"given":"Xiao-Yuan","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, China, School of Computer Science and Technology, Zhejiang Sci-Tech University, China, the State Key Laboratory for Novel Software Technology, Nanjing University, China, and Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis and School of Computer, Guangdong University of Petrochemical Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Australia and School of Big Data and Software Engineering, Chongqing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7477-3642","authenticated-orcid":false,"given":"Yang","family":"Feng","sequence":"additional","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9255-6772","authenticated-orcid":false,"given":"Haowen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4645-2526","authenticated-orcid":false,"given":"Yuming","family":"Zhou","sequence":"additional","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7743-1296","authenticated-orcid":false,"given":"Baowen","family":"Xu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2018. 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