Automated RPA Code Review System for Compliance and Security
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Solution Overview
Problem
Traditional RPA code review is difficult and time-consuming due to its complex structure, requiring human subjectivity and manual analysis of GUI objects/commands, which can lead to inefficiencies and potential security vulnerabilities.
Innovation Solution
An automated RPA Analysis and Review System utilizing machine learning and AI to scan RPA files against defined coding standards, providing an efficient code review report and enabling automatic modifications to ensure compliance, reducing reliance on human subjectivity and streamlining the review process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual code review is performed, then human subjectivity and flexibility are maintained, but time consumption and inefficiency increase
Solution Approach 1:
The system enables self-service code review by automatically analyzing RPA code against coding standards and security vulnerabilities without requiring manual intervention. The automated review system performs the entire code analysis process independently, eliminating the need for human reviewers to manually examine code while maintaining comprehensive coverage of coding standards and security requirements.
Solution Approach 2:
The patent replaces the mechanical manual code review process with an automated system that uses machine learning models and AI algorithms to perform code analysis. The system substitutes human-based mechanical review with automated computational analysis, achieving both speed and comprehensive coverage of coding standards and security vulnerabilities.
2Productivity
If automated code review is implemented, then time efficiency is improved, but complexity of the review system increases
Solution Approach 1:
The automated review system achieves multi-functionality by simultaneously performing multiple code analysis tasks including coding standard compliance checking, security vulnerability detection, and code quality assessment. A single unified system handles all these functions through integrated machine learning models, avoiding the need for separate specialized tools and reducing overall system complexity despite the advanced capabilities employed.
3Measurement precision
If manual analysis of GUI objects/commands is performed, then detailed inspection is possible, but difficulty and subjectivity increase
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models continuously learn from code analysis results and automatically adjust their detection criteria. The system provides detailed inspection of GUI objects and commands while maintaining objective, data-driven analysis through feedback loops that refine detection accuracy and eliminate human subjectivity from the code review process.
Data Source
AI summary
Systems and methods for analyzing Robotic Process Automation (RPA) code are provided. A robotic process automation (RPA) file is received. RPA data within the RPA file is parsed and restructured for rendering in a code-review graphical user interface (GUI). A code-review GUI with the restructured RPA data is then rendered.