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AAT4IRS: automated acceptance testing for industrial robotic systems.
Marcela G Dos Santos1, Sylvain Hallé1, Fabio Petrillo2
1Départment d'Informatique et Mathématique, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada.
Frontiers in Robotics and AI
|October 18, 2024
Summary
Automated acceptance testing for industrial robotic systems (AAT4IRS) uses natural language to enhance software testing. This approach effectively detects 79% of faults in industrial robot software, ensuring system safety and reliability.
Area of Science:
- Robotics
- Software Engineering
- Industrial Automation
Background:
- Industrial robotic systems (IRS) automate critical industrial processes, but failures can be catastrophic.
- Ensuring the quality and safety of IRS software through testing is crucial.
- Existing software testing methods face challenges in diverse teams and complex industrial integration.
Purpose of the Study:
- To propose a novel software testing approach for industrial robotic systems.
- To address challenges in testing complex IRS through natural language processing.
- To enhance the reliability and safety of industrial robot software.
Main Methods:
- Developed Automated Acceptance Testing for Industrial Robotic Systems (AAT4IRS) using natural language.
- Utilized behavior-driven development principles with user stories and scenarios.
- Evaluated AAT4IRS effectiveness using a pick-and-place process and mutation testing.
Main Results:
- AAT4IRS demonstrated high effectiveness in detecting software faults.
- The implemented test suites successfully detected 79% of generated mutants.
- Mutation testing confirmed the robustness of the AAT4IRS approach.
Conclusions:
- AAT4IRS provides a robust and effective method for testing industrial robotic systems.
- The use of natural language facilitates collaboration and improves test suite quality.
- This approach instills confidence in the safety and reliability of industrial robot software.
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