Autonomous Software Testing via Reinforcement Learning
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Solution Overview
Problem
Current software testing methods require significant manual effort and time, with no mechanism for automatic test case generation, making them inefficient, especially in the context of rapid technological advancements.
Innovation Solution
An autonomous testing method using a reinforcement learning engine to identify critical paths and generate test scripts, which are then executed to capture results and update a reward matrix, reducing manual effort and improving testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual test case writing and execution is used, then testing can be performed with basic tools, but testing time and manual effort increase significantly
Solution Approach 1:
The system enables autonomous testing where the testing system automatically generates test cases, identifies critical paths, and executes tests without human intervention. The reinforcement learning engine learns from test results and autonomously improves testing strategies, making the system self-improving and eliminating dependency on manual tester involvement.
Solution Approach 2:
The patent replaces manual mechanical processes of test case writing and execution with automated intelligent systems. The reinforcement learning engine and application crawler substitute human testers' mechanical actions with algorithmic processes that automatically navigate applications, generate test cases, and analyze results.
2Productivity
If test automation tools are used, then test execution time is reduced, but test case generation still requires manual effort
Solution Approach 1:
The system autonomously generates test cases by having the application crawler automatically explore the application interface and the reinforcement learning engine identify critical paths. This self-service mechanism eliminates the need for testers to manually create test cases, as the system generates them automatically based on learned patterns and criticality analysis.
Solution Approach 2:
The patent introduces an intermediary reinforcement learning engine that acts as a bridge between the application structure and test case generation. This intermediary analyzes application states, calculates rewards based on criticality, and generates optimized test cases, replacing the direct manual process with an intelligent intermediate layer.
3Reliability
If comprehensive testing is performed to ensure quality, then testing coverage improves, but testing time and resources increase
Solution Approach 1:
The system applies local quality by focusing testing efforts on critical paths rather than uniformly testing all application paths. The reinforcement learning engine calculates rewards based on path criticality and error rates, directing comprehensive testing resources to high-risk areas while reducing coverage on low-risk paths, thereby achieving effective coverage with reduced time.
Solution Approach 2:
The patent dynamically changes testing parameters based on learned information. The reinforcement learning engine adjusts test case priorities, path selection criteria, and exploration strategies based on accumulated knowledge from previous tests, allowing the system to adapt testing coverage and duration to actual application characteristics and risk profiles.
Data Source
AI summary
A method and a system for automated testing of applications includes crawling an application by an application crawler to identify application states. Rewards associated with the application states are calculated using a reinforcement learning engine, based on a reward matrix. Critical paths are identified by the reinforcement learning engine and are passed to a test scenario generator. Test scripts are generated by the test scenario generator based on the identified critical paths. The applications are tested by a test scenario execution engine based on the generated one or more test scripts, and test data generated by the test data generator. Test results are captured by a behavior analyzer. One or more insights are generated by the behavior analyzer, from the captured test results to update the reward matrix and to improve the efficiency of continuous autonomous testing system.