Testcase Automation Generator for Storage Systems
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Solution Overview
Problem
Designing tests for storage systems is labor-intensive and relies heavily on personal experience, leading to potential inaccuracies in determining whether new features or functions are operating correctly, which can result in data loss or unavailability, especially in critical data storage environments.
Innovation Solution
A method and apparatus that utilize a learning function trained on metadata of previously generated testcases to predict subsequent steps and eliminate duplicates, by parsing metadata into steptasks and using a deep learning network to determine relationships and suggest next steps in the testcase creation process, aided by a testcase rules engine for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testcase creation is used, then personal experience can be applied, but labor intensity increases and accuracy decreases
Solution Approach 1:
The system enables self-service testcase generation by automatically analyzing storage system metadata, identifying test scenarios, and creating testcases without requiring manual intervention from testers, thereby improving both accuracy and productivity
Solution Approach 2:
The patent replaces the mechanical process of manual testcase creation with an automated system that uses machine learning models and natural language processing to generate testcases, eliminating human labor while maintaining or improving accuracy
2Reliability
If more testcases are created to improve coverage, then testing completeness increases, but duplicate tests increase and efficiency decreases
Solution Approach 1:
The system implements feedback mechanisms where test execution results are automatically analyzed and used to refine the testcase generation process, ensuring comprehensive coverage while eliminating redundant tests through continuous learning and adaptation
Solution Approach 2:
The patent changes the parameters of testcase generation by using dynamic thresholds and criteria based on system state, allowing the system to generate appropriate数量的 testcases without creating duplicates, thereby maintaining reliability while improving efficiency
Data Source
AI summary
Testcase recommendations are generated for a testcase creator application by training a learning function using metadata of previously generated testcases by parsing the metadata into steptasks, and providing the parsed metadata to the learning function to enable the learning function to determine relationships between the steptasks of the previously generated testcases, and using, by the testcase creator application, the trained learning function to obtain a predicted subsequent steptask for a given type of testcase to be generated. Each steptask describes one of the steps of the testcase using a concatenation of a step number of the one of the steps of the testcase, a module and a submodule to be used to perform of the one of the steps of the testcase, and a function to be performed at the one of the steps of the testcase.