Autonomous Software Testing Robot for Storage Defect Detection
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Solution Overview
Problem
Conventional approaches to tuning test parameters in complex information processing systems are inefficient, time-consuming, and costly, relying on manual tuning and lacking the ability to share tuning experience and learn from previous tests.
Innovation Solution
A tuning system that includes a tuning agent and assessment module to execute test cases based on a state associated with a test case, using reinforcement learning to determine optimal tuning actions by comparing subsequent executions with previous ones, thereby iteratively learning and adapting for similar testing efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of test parameters is performed, then tuning experience can be applied, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service through automated reinforcement learning agents that independently tune test parameters without manual intervention. The tuning agent continuously learns from test outcomes and autonomously adjusts parameters to optimize defect detection, eliminating the need for manual tuning while maintaining high reliability.
Solution Approach 2:
The system implements feedback mechanisms where test results are continuously fed back to the reinforcement learning agent. The agent uses this feedback to assess the value of tuning actions and iteratively improves parameter selection, creating a closed-loop system that automatically enhances defect detection capability over time.
2Measurement precision
If extensive parameter combinations are tested, then optimal parameters can be identified, but the cost and time increase significantly
Solution Approach 1:
The system dynamically changes test parameters based on reinforcement learning assessments rather than exhaustively testing all combinations. The agent selectively adjusts parameters like test object scale, workload, and duration based on learned patterns from previous tests, achieving high optimization accuracy with significantly fewer test iterations.
Solution Approach 2:
The system performs preliminary actions by pre-assessing the value of potential tuning actions using reinforcement learning before actual testing. This allows the system to prioritize high-value parameter combinations and avoid wasting resources on low-probability test cases, improving testing efficiency while maintaining optimization accuracy.
3Reliability
If manual tuning experience is used, then expertise can be applied, but the experience cannot be shared or learned from
Solution Approach 1:
The reinforcement learning system serves itself by automatically capturing, storing, and learning from tuning outcomes. Each test result automatically updates the agent's knowledge base, enabling continuous improvement and automatic sharing of tuning insights across the system without requiring manual documentation or transfer of expertise.
Solution Approach 2:
The system replaces manual mechanical tuning processes with automated computational reinforcement learning. This substitution transforms subjective human expertise into objective, replicable algorithms that can be consistently applied and shared across different testing scenarios, enhancing both reliability and adaptability.
Data Source
AI summary
Methods, system, and non-transitory processor-readable storage medium for a tuning system are provided herein. An example method includes providing, by a tuning module, a tuning action based on a state associated with a test case, executing, by a tuning agent, the test case using the tuning action, assessing, by a tuning assessment module, the tuning action with respect to a long-term reward, and determining a similarity between a subsequent execution of the test case and the assessment of the tuning action on at least one previous execution of the test case to tune the tuning action.