Zhou, Yu ; Jin, Haonan ; Yang, Xinying ; Chen, Taolue ; Narasimhan, Krishna ; Gall, Harald C.
Hrsg.: Spinellis, Diomidis ; Gousios, Georgios ; Chechik, Marsha ; Penta, Massimiliano Di (2021)
BRAID: an API recommender supporting implicit user feedback.
29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Athens, Greece (23.-28.08.2021)
doi: 10.1145/3468264.3473111
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Efficient application programming interface (API) recommendation is one of the most desired features of modern integrated development environments. A multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we present BRAID (Boosting RecommendAtion with Implicit FeeDback), a tool which leverages user feedback, and employs learning-to-rank and active learning techniques to boost recommendation performance. The implementation is based on the VSCode plugin architecture, which provides an integrated user interface. Essentially, BRAID is a general framework which can accommodate existing query-based API recommendation approaches as components. Comparative experiments with strong baselines demonstrate the efficacy of the tool. A video demonstrating the usage of BRAID can be found at https://youtu.be/naD0guvl8sE.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Herausgeber: | Spinellis, Diomidis ; Gousios, Georgios ; Chechik, Marsha ; Penta, Massimiliano Di |
| Autor(en): | Zhou, Yu ; Jin, Haonan ; Yang, Xinying ; Chen, Taolue ; Narasimhan, Krishna ; Gall, Harald C. |
| Art des Eintrags: | Bibliographie |
| Titel: | BRAID: an API recommender supporting implicit user feedback |
| Sprache: | Englisch |
| Publikationsjahr: | 18 August 2021 |
| Ort: | New York, NY, United States |
| Verlag: | ACM |
| Buchtitel: | ESEC/FSE'21: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
| Veranstaltungstitel: | 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
| Veranstaltungsort: | Athens, Greece |
| Veranstaltungsdatum: | 23.-28.08.2021 |
| DOI: | 10.1145/3468264.3473111 |
| Kurzbeschreibung (Abstract): | Efficient application programming interface (API) recommendation is one of the most desired features of modern integrated development environments. A multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we present BRAID (Boosting RecommendAtion with Implicit FeeDback), a tool which leverages user feedback, and employs learning-to-rank and active learning techniques to boost recommendation performance. The implementation is based on the VSCode plugin architecture, which provides an integrated user interface. Essentially, BRAID is a general framework which can accommodate existing query-based API recommendation approaches as components. Comparative experiments with strong baselines demonstrate the efficacy of the tool. A video demonstrating the usage of BRAID can be found at https://youtu.be/naD0guvl8sE. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Softwaretechnik |
| Hinterlegungsdatum: | 05 Mär 2024 15:29 |
| Letzte Änderung: | 11 Jun 2024 14:28 |
| PPN: | 51903676X |
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