A Pilot Study of a Human-Readable Robotic Process Automation Language
November 07, 2023 Β· Declared Dead Β· π Multimedia, Interaction, Design and Innovation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Piotr Gago, Daniel JabΕoΕski, Anna Voitenkova, Ihor Debelyi, Kinga Skorupska, Maciej Grzeszczuk, WiesΕaw KopeΔ
arXiv ID
2311.04328
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
Multimedia, Interaction, Design and Innovation
Last Checked
5 months ago
Abstract
In this paper, we explore the usability of a custom eXtensible Robotic Language (XRL) we proposed. To evaluate the user experience and the interaction with the potential XRL-based software robot, we conducted an exploratory study comparing the notation of three business processes using our XRL language and two languages used by the leading RPA solutions. The results of our exploratory study show that the currently used XML-based formats perform worse in terms of conciseness and readability. Our new XRL language is promising in terms of increasing the readability of the language, thus reducing the time needed to automate business processes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted