AI Workflow Analyzer for RPA Test Automation Flaws
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Solution Overview
Problem
Current RPA tools lack the ability to effectively analyze test automation workflows for identifying and removing potential flaws, leading to time-consuming and costly manual testing and debugging processes.
Innovation Solution
A computer-implemented method and system using an AI model within a workflow analyzer module to analyze test automation workflows based on pre-defined rules, determining metrics, and generating corrective activity data to identify and rectify flaws before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing of test automation workflows is performed, then flaws can be identified, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical testing processes with an automated AI-based analysis system. The workflow analyzer module uses machine learning models to automatically analyze test automation workflows, substituting human testers with an intelligent system that can process workflows faster and more consistently without manual intervention.
Solution Approach 2:
The patent introduces a workflow analyzer module as an intermediary between workflow development and manual testing. This intermediary component automatically analyzes workflows using AI models and generates reports, acting as a bridge that reduces the need for direct human involvement in the testing process while maintaining thorough flaw identification.
2Reliability
If debugging of flaws in test automation workflows is performed at real-time, then run-time errors can be avoided, but the process becomes more challenging
Solution Approach 1:
The patent performs preliminary analysis of test automation workflows using AI models before execution. The workflow analyzer module examines workflows in advance, identifying potential flaws and generating corrective activity data before the workflows run, thereby preventing errors before they occur rather than debugging them during execution.
Solution Approach 2:
The patent implements a feedback mechanism where the AI model analyzes workflows and generates corrective activity data that is fed back to developers. This feedback loop provides actionable insights about potential flaws and suggested corrections, making the debugging process more systematic and less complex by guiding developers through specific issues and solutions.
3Productivity
If AI model analysis is applied to test automation workflows, then flaws can be identified efficiently, but computational resources are required
Solution Approach 1:
The patent applies partial analysis by focusing the AI model's attention on specific aspects of workflows that are most prone to errors. Rather than analyzing every single element in exhaustive detail, the system targets high-risk areas and critical path elements, achieving effective flaw identification with reduced computational overhead compared to complete exhaustive analysis.
Data Source
AI summary
A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.