RPA Workflow Test Execution for Application Change Detection
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Solution Overview
Problem
Robotic process automation (RPA) systems face challenges in maintaining stability due to application and environment changes, lacking effective maintenance and exception handling, leading to frequent bot failures.
Innovation Solution
A computer-implemented method for creating and executing test cases for existing workflows to identify environmental and automation issues, reporting failed tests, and notifying users, which includes creating test cases for workflows, executing them to detect issues, and providing results with failure notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If RPA bots are implemented to automate processes, then productivity is improved, but reliability deteriorates due to application changes and environment changes
Solution Approach 1:
The system performs preliminary actions by automatically detecting application changes and environment changes before they cause bot failures. Change detection mechanisms monitor applications and environments proactively, identifying modifications ahead of time so that bots can be updated or adjusted before stability is affected
Solution Approach 2:
The system implements feedback loops where bot execution results, error logs, and performance metrics are continuously analyzed. This feedback enables automatic adjustment of bot parameters and triggers alerts when reliability thresholds are breached, allowing the system to self-correct and maintain stable operation despite external changes
2Ease of operation
If RPA implementations focus on happy paths, then ease of operation is improved, but reliability deteriorates due to lack of exception handling
Solution Approach 1:
The system segments the automation workflow into distinct components: happy path execution, exception detection zones, and error handling modules. This segmentation allows the main workflow to remain simple and easy to implement while dedicated exception handling segments manage reliability concerns separately
Solution Approach 2:
The system prepares exception handling mechanisms in advance by pre-defining error handlers, fallback procedures, and recovery protocols for anticipated failure scenarios. This cushioning ensures that when exceptions occur, the system can gracefully handle them without compromising overall reliability
3Reliability
If manual testing of workflows is performed, then reliability is improved through issue detection, but productivity deteriorates due to increased time consumption
Solution Approach 1:
The system performs self-testing by automatically executing test cases, analyzing results, and generating reports without human intervention. The automated testing framework independently validates workflow correctness, detects issues, and even self-corrects certain problems, eliminating the need for manual testing while maintaining high reliability
Solution Approach 2:
The system implements continuous automated testing that runs alongside bot execution, providing ongoing validation rather than periodic manual checks. This continuous action ensures issues are detected immediately upon occurrence, maintaining reliability without interrupting productivity
Data Source
AI summary
Test cases for existing workflows (or workflows under test) may be created and executed. A test case may be created for a workflow in production or one or more parts of the workflow, and the created test case for the workflow, or the one or more parts of the workflow, may be executed to identify environmental and/or automation issues for the workflow. A failed workflow test may be reported when the environmental and/or automation issues are identified.