TU Darmstadt / ULB / TUbiblio

BRAID: an API recommender supporting implicit user feedback

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
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen