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BRAID: an API recommender supporting implicit user feedback

Zhou, Yu ; Jin, Haonan ; Yang, Xinying ; Chen, Taolue ; Narasimhan, Krishna ; Gall, Harald C.
eds.: 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
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2021
Editors: Spinellis, Diomidis ; Gousios, Georgios ; Chechik, Marsha ; Penta, Massimiliano Di
Creators: Zhou, Yu ; Jin, Haonan ; Yang, Xinying ; Chen, Taolue ; Narasimhan, Krishna ; Gall, Harald C.
Type of entry: Bibliographie
Title: BRAID: an API recommender supporting implicit user feedback
Language: English
Date: 18 August 2021
Publisher: ACM
Book Title: ESEC/FSE'21: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Event Title: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Event Location: Athens, Greece
Event Dates: 23.-28.08.2021
DOI: 10.1145/3468264.3473111
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.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Software Technology
Date Deposited: 05 Mar 2024 15:29
Last Modified: 05 Mar 2024 15:29
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