Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2020

Abstract

Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way these recommender systems interact with developers is often rudimentary—a long list of recommendations only ranked by the model’s confidence. In this vision paper, we lay out our research agenda for re-imagining how recommender systems for software engineering communicate their insights to developers. When issuing recommendations, our aim is to recommend diverse rather than redundant solutions and present them in ways that highlight their differences. We also want to allow for seamless and interactive navigation of suggestions while striving for holistic end-to-end evaluations. By doing so, we believe that recommender systems can play an even more important role in helping developers write better software.

Keywords

API; Documentation, Knowledge Extraction, Knowledge Graph

Discipline

Graphics and Human Computer Interfaces | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE): Virtual Conference, 2020 September 21-25

First Page

834

Last Page

845

ISBN

9781450367684

Identifier

10.1145/3324884.3416628

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3324884.3416628

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