Grammar-Based Fault Detection Using Feedback-Driven Test Refinement
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Solution Overview
Problem
Traditional grammar-based test generation methods produce excessively large sets of test cases, requiring excessive resources and failing to efficiently test systems due to the need for manual editing and the generation of overly extensive test sequences.
Innovation Solution
An automated testing framework that modifies or creates new grammars by controlling attribute tags based on feedback from the system under test, allowing for the generation of a focused subset of test cases that effectively identify faults while reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional grammar-based test generation is used, then comprehensive test coverage is achieved, but the number of test cases becomes excessively large requiring excessive resources
Solution Approach 1:
The patent extracts and removes redundant test cases from the grammar-generated test suite by analyzing execution feedback. The system identifies and eliminates duplicate or unnecessary test cases while preserving those that reveal faults, thereby reducing the overall quantity of test cases while maintaining comprehensive fault detection coverage.
Solution Approach 2:
The patent implements dynamic test case generation and refinement based on feedback from system execution. The grammar is dynamically modified during the testing process to generate focused subsets of test cases, adapting the test suite size and composition based on actual system behavior and fault detection needs rather than generating a static exhaustive set upfront.
2Reliability
If traditional grammar-based test generation is used, then all possible test sequences are generated, but testing system resources are excessively consumed
Solution Approach 1:
The patent employs feedback mechanisms where the testing system monitors execution results and uses this information to dynamically refine the grammar and generate subsequent test cases. This feedback-driven approach ensures that testing resources are focused on generating test cases that are most likely to reveal faults, rather than exhaustively generating all possible test sequences upfront.
Solution Approach 2:
The patent generates a focused subset of test cases that is sufficient for effective fault detection rather than generating the complete exhaustive set. By using feedback to identify and prioritize test cases that reveal faults, the system performs partial action that achieves the necessary testing coverage without the excessive resource consumption of complete exhaustion.
3Reliability
If the grammar generates a large set of strings, then comprehensive testing is possible, but manual editing becomes necessary and productivity decreases
Solution Approach 1:
The patent implements self-service automation where the testing system automatically generates, executes, analyzes, and refines test cases based on feedback without requiring manual intervention. The grammar is automatically modified and new test cases are generated based on execution results, eliminating the need for manual editing while maintaining comprehensive testing coverage.
Solution Approach 2:
The patent performs preliminary automated analysis of test case effectiveness and automatically refines the grammar before full-scale testing. By pre-processing and optimizing the test case generation based on initial feedback, the system reduces the need for subsequent manual editing and improves overall testing productivity while maintaining completeness.
Data Source
AI summary
The present disclosure relates to automated testing of hardware and software systems. In some embodiments, a testing framework that generates a set of test cases for a system under test using a grammar is disclosed. The grammar may include terminal and nonterminal symbols with tags that describe a test sequence. The testing framework can use the grammar to generate a set of test cases. The testing framework can then receive feedback about the execution of the set of test cases from the system under test. In response to the feedback, the testing framework can generate a new set of grammars by automatically modifying or inserting tags in the original grammar. The new set of grammars can then be used to generate further test cases that intelligently explore the system under test for correctness, conformance, performance, security, or reliability.