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Wed 11 Nov 2020 17:30 - 17:32 at Virtual room 1 - Recommendation

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 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change

17:30 - 17:32
Talk
API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Research Papers
Wenkai XieFudan University, China, Xin PengFudan University, China, Mingwei LiuFudan University, China, Christoph TreudeUniversity of Adelaide, Australia, Zhenchang XingAustralian National University, Australia, Xiaoxin ZhangFudan University, China, Wenyun ZhaoFudan University, China
DOI
17:33 - 17:34
Talk
Code Recommendation for Exception Handling
Research Papers
Tam NguyenAuburn University, USA, Phong VuAuburn University, USA, Tung NguyenAuburn University, USA
DOI
17:35 - 17:36
Talk
eQual: Informing Early Design Decisions
Research Papers
Arman ShahbazianGoogle, USA, Suhrid KarthikUniversity of Southern California, USA, Yuriy BrunUniversity of Massachusetts Amherst, Nenad MedvidovićUniversity of Southern California, USA
Link to publication DOI Pre-print Media Attached
17:37 - 17:38
Talk
Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching
Research Papers
Sonal MahajanFujitsu Labs, USA, Negarsadat AbolhassaniUniversity of Southern California, USA, Mukul R. PrasadFujitsu Labs, USA
DOI Pre-print Media Attached
17:39 - 17:40
Talk
Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards
Student Research Competition
Dragos StrugarInnopolis University, Russia
DOI
17:41 - 17:42
Talk
Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
Research Papers
Chris BrownNorth Carolina State University, USA, Chris ParninNorth Carolina State University, USA
DOI
17:43 - 18:00
Talk
Conversations on Recommendation
Paper Presentations
Chris BrownNorth Carolina State University, USA, Dragos StrugarInnopolis University, Russia, Mingwei LiuFudan University, China, Sonal MahajanFujitsu Labs, USA, Arman ShahbazianUniversity of Southern California, M: Massimiliano Di PentaUniversity of Sannio, Italy