Autonomous Software Testing Agent for Script-Free Exploration
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Solution Overview
Problem
Existing automated testing tools are limited in their ability to perform exploratory testing, requiring manual script creation and being fragile to application changes, leading to increased costs and inefficiencies.
Innovation Solution
An autonomous testing agent that navigates applications, dynamically generates test actions, decouples actions from expected results, and uses generalized rules to provide goal-directed testing, eliminating the need for pre-defined scripts and enhancing test coverage and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scripted testing with automated test tools is used, then testing speed and repeatability are improved, but test script development labor and cost increase, and test scripts become fragile to application changes
Solution Approach 1:
The system enables self-service testing by allowing the application under test to generate its own test scripts through metadata extraction and rule-based test generation. The application's own metadata and business rules serve as the foundation for automatic test case creation, eliminating the need for external test script developers and reducing script development time while maintaining high testing speed and repeatability
2Reliability
If scripted testing with automated test tools is used, then testing repeatability is improved, but adaptability to application changes deteriorates
Solution Approach 1:
The system implements dynamic test script generation by continuously extracting metadata from the application and regenerating test scripts based on current application state. When applications change, the metadata extraction automatically detects updates and regenerates relevant test scripts, maintaining repeatability while adapting to changes without manual intervention
Solution Approach 2:
The system performs preliminary metadata extraction and analysis to proactively prepare test scripts that are inherently adapted to the application's current structure. By continuously monitoring and extracting metadata before testing occurs, the system ensures test scripts are always synchronized with the application state, improving both repeatability and adaptability
3Reliability
If manual exploratory testing is used, then testing completeness and rigor are improved, but testing time and cost increase
Solution Approach 1:
The system enables self-service exploratory testing by automatically generating comprehensive test cases from application metadata and business rules. The application itself provides the test logic through its metadata, eliminating the need for manual test design while maintaining thorough test coverage and completeness that would otherwise require extensive manual exploratory testing time
4Productivity
If automated test scripts are used, then labor cost for running tests is reduced, but maintenance cost increases due to script fragility
Solution Approach 1:
The system implements dynamic test script regeneration by continuously extracting metadata from the application and automatically updating test scripts when changes are detected. This eliminates script fragility issues because test scripts are not static but are continuously regenerated from the application's current metadata state, maintaining high testing efficiency while reducing maintenance time to near zero
Solution Approach 2:
The system establishes feedback loops where test execution results and metadata extraction continuously inform script regeneration. When applications change, the system detects these changes through metadata comparison and automatically regenerates affected test scripts, creating a self-correcting system that eliminates maintenance burden while preserving testing efficiency
Data Source
AI summary
A system and method for performing autonomous testing of software applications is provided. The system is comprised of an autonomous testing agent; a generalized representation of components, actions, rules, and flows that can be dynamically mapped to application-specific instances; a work queue for items to be tested; a dynamically-constructed model of the application under test; and a log of testing actions and results. The autonomous agent navigates the application under test, discovers interface components and actions, places components and actions in the work queue and/or adds them to the model, executes actions against interface components, applies rules to verify that expected behaviors were exhibited, and logs testing actions and results. The system provides an objective measure of testing by aggregating testing actions and results relative to the application model.