RecipeGen++: An automated trigger action programs generator

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 d...

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Main Authors: YUSUF, Imam Nur Bani, ABDUL JAMAL, Diyanah, JIANG, Lingxiao, LO, David
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7634
https://ink.library.smu.edu.sg/context/sis_research/article/8637/viewcontent/fse22RecipeGenDemo.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-86372023-03-31T01:26:01Z RecipeGen++: An automated trigger action programs generator YUSUF, Imam Nur Bani ABDUL JAMAL, Diyanah JIANG, Lingxiao LO, David 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. 2022-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7634 info:doi/10.1145/3540250.3558913 https://ink.library.smu.edu.sg/context/sis_research/article/8637/viewcontent/fse22RecipeGenDemo.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University software engineering trigger action deep learning natural language processing Artificial Intelligence and Robotics Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic software engineering
trigger action
deep learning
natural language processing
Artificial Intelligence and Robotics
Software Engineering
spellingShingle software engineering
trigger action
deep learning
natural language processing
Artificial Intelligence and Robotics
Software Engineering
YUSUF, Imam Nur Bani
ABDUL JAMAL, Diyanah
JIANG, Lingxiao
LO, David
RecipeGen++: An automated trigger action programs generator
description 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.
format text
author YUSUF, Imam Nur Bani
ABDUL JAMAL, Diyanah
JIANG, Lingxiao
LO, David
author_facet YUSUF, Imam Nur Bani
ABDUL JAMAL, Diyanah
JIANG, Lingxiao
LO, David
author_sort YUSUF, Imam Nur Bani
title RecipeGen++: An automated trigger action programs generator
title_short RecipeGen++: An automated trigger action programs generator
title_full RecipeGen++: An automated trigger action programs generator
title_fullStr RecipeGen++: An automated trigger action programs generator
title_full_unstemmed RecipeGen++: An automated trigger action programs generator
title_sort recipegen++: an automated trigger action programs generator
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7634
https://ink.library.smu.edu.sg/context/sis_research/article/8637/viewcontent/fse22RecipeGenDemo.pdf
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