A Unified Framework for Automated Testing of Robotic Process Automation Workflows Using Symbolic and Concolic Analysis

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 f...

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Vydáno v:Machines (Basel) Ročník 13; číslo 6; s. 504
Hlavní autoři: Paduraru, Ciprian, Cernat, Marina, Staicu, Adelina-Nicoleta
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2025
Témata:
automated user interface testing
Automation
Best practice
concolic testing
Hypotheses
Information systems
Large language models
Mechanization
Methods
Program verification (computers)
robotic process automation
robotic process automation debugging
Robotics
Software
symbolic execution
User interface
User interfaces
Web applications
workflow testing
Nova Scotia
ISSN:2075-1702, 2075-1702
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Shrnutí: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.
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ISSN:2075-1702
2075-1702
DOI:10.3390/machines13060504