Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2022

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

information representation, information retrieval diversity, Recommender systems, software engineering, user interaction

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, October 10-14

First Page

1

Last Page

5

ISBN

9781450396240

Identifier

10.1145/3551349.3559557

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3551349.3559557

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