Content area

Abstract

Robotic Process Automation is a technology that replicates human interactions with user interfaces across various applications. However, testing Robotic Process Automation implementations remains challenging due to the dynamic nature of workflows. This paper presents a novel testing framework that first integrates symbolic execution and concolic testing strategies to enhance Robotic Process Automation workflow validation. Building on insights from these methods, we introduce a hybrid approach that optimizes test coverage and efficiency in specific cases. Our open-source implementation demonstrates that automated testing in the Robotic Process Automation domain significantly improves coverage, reduces manual effort, and enhances reliability. Furthermore, the proposed solution supports multiple Robotic Process Automation platforms and aligns with industry best practices for user interface automation testing. Experimental evaluation, conducted in collaboration with industry, validates the effectiveness of our approach.

Details

1009240
Business indexing term
Title
A Unified Framework for Automated Testing of Robotic Process Automation Workflows Using Symbolic and Concolic Analysis †
Publication title
Machines; Basel
Volume
13
Issue
6
First page
504
Number of pages
29
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
e-ISSN
20751702
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-06-09
Milestone dates
2025-04-14 (Received); 2025-06-07 (Accepted)
Publication history
 
 
   First posting date
09 Jun 2025
ProQuest document ID
3223924754
Document URL
https://www.proquest.com/scholarly-journals/unified-framework-automated-testing-robotic/docview/3223924754/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-06-25
Database
ProQuest One Academic