API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Due to the lexical gap between functionality descriptions and user queries, documentation-based API retrieval often produces poor results.Verb phrases and their phrase patterns are essential in both describing API functionalities and interpreting user queries. Thus we hypothesize that API retrieval can be facilitated by explicitly recognizing and matching between the fine-grained structures of functionality descriptions and user queries. To verify this hypothesis, we conducted a large-scale empirical study on the functionality descriptions of 14,733 JDK and Android API methods. We identified 356 different functionality verbs from the descriptions, which were grouped into 87 functionality categories, and we extracted 523 phrase patterns from the verb phrases of the descriptions. Building on these findings, we propose an API method recommendation approach based on explicit matching of functionality verb phrases in functionality descriptions and user queries, called PreMA. Our evaluation shows that PreMA can accurately recognize the functionality categories (92.8%) and phrase patterns (90.4%) of functionality description sentences; and when used for API retrieval tasks, PreMA can help participants complete their tasks more accurately and with fewer retries compared to a baseline approach.
Wed 11 NovDisplayed time zone: (UTC) Coordinated Universal Time change
17:30 - 18:00 | RecommendationStudent Research Competition / Research Papers / Paper Presentations at Virtual room 1 | ||
17:30 2mTalk | API Method Recommendation via Explicit Matching of Functionality Verb Phrases Research Papers Wenkai Xie Fudan University, China, Xin Peng Fudan University, China, Mingwei Liu Fudan University, China, Christoph Treude University of Adelaide, Australia, Zhenchang Xing Australian National University, Australia, Xiaoxin Zhang Fudan University, China, Wenyun Zhao Fudan University, China DOI | ||
17:33 1mTalk | Code Recommendation for Exception Handling Research Papers Tam Nguyen Auburn University, USA, Phong Vu Auburn University, USA, Tung Nguyen Auburn University, USA DOI | ||
17:35 1mTalk | eQual: Informing Early Design Decisions Research Papers Arman Shahbazian Google, USA, Suhrid Karthik University of Southern California, USA, Yuriy Brun University of Massachusetts Amherst, Nenad Medvidović University of Southern California, USA Link to publication DOI Pre-print Media Attached | ||
17:37 1mTalk | Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching Research Papers Sonal Mahajan Fujitsu Labs, USA, Negarsadat Abolhassani University of Southern California, USA, Mukul Prasad Fujitsu Labs, USA DOI Pre-print Media Attached | ||
17:39 1mTalk | Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards Student Research Competition Dragos Strugar Innopolis University, Russia DOI | ||
17:41 1mTalk | Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers Research Papers DOI | ||
17:43 17mTalk | Conversations on Recommendation Paper Presentations Chris Brown North Carolina State University, USA, Dragos Strugar Innopolis University, Russia, Mingwei Liu Fudan University, China, Sonal Mahajan Fujitsu Labs, USA, Arman Shahbazian University of Southern California, M: Massimiliano Di Penta University of Sannio, Italy | ||