Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2022

Abstract

Trigger Action Programs (TAPs) are event-driven rules that allow users to automate smart-devices and internet services. Users can write TAPs by specifying triggers and actions from a set of predefined channels and functions. Despite its simplicity, composing TAPs can still be challenging for users due to the enormous search space of available triggers and actions. The growing popularity of TAPs is followed by the increasing number of supported devices and services, resulting in a huge number of possible combinations between triggers and actions. Motivated by such a fact, we improve our prior work and propose RecipeGen++, a deep-learning-based approach that leverages Transformer seq2seq (sequence-to-sequence) architecture to generate TAPs given natural language descriptions. RecipeGen++ can generate TAPs in the Interactive, One-Click, or Functionality Discovery modes. In the Interactive mode, users can provide feedback to guide the prediction of a trigger or action component. In contrast, the One-Click mode allows users to generate all TAP components directly. Additionally, RecipeGen++ also enables users to discover functionalities at the channel level through the Functionality Discovery mode. We have evaluated RecipeGen++ on real-world datasets in all modes. Our results demonstrate that RecipeGen++ can outperform the baseline by 2.2%-16.2% in the gold-standard benchmark and 5%-29.2% in the noisy benchmark.

Keywords

software engineering, trigger action, deep learning, natural language processing

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Singapore, November 14-18

First Page

1672

Last Page

1676

ISBN

9781450394130

Identifier

10.1145/3540250.3558913

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1145/3540250.3558913

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