Real-Time Anomalous Behavior Identification in Software Programs
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Solution Overview
Problem
There is no standard mechanism for identifying which test cases from a test suite are applicable for testing a software product in a production environment, making it challenging to ensure the quality of software products that have undergone environmental changes or updates.
Innovation Solution
A system and method that generate signature files for test cases in a staging environment, capture real-time trace data in a production environment, and compare it with the signature files to identify anomalous behavior, allowing for real-time identification of applicable test cases and potential bugs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire set of test cases is used to test the software product in the production environment, then the quality assurance is improved, but the testing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the entire test suite into multiple subsets by creating signature files for each test case. The system then selects and executes only the relevant subset of test cases based on the specific production environment context, rather than running all test cases. This segmentation approach maintains quality assurance while significantly reducing testing time and resource consumption.
Solution Approach 2:
The patent implements partial action by executing only the necessary portion of the test suite required for effective quality assurance in the production environment. The signature-based selection mechanism identifies and runs only the relevant test cases, avoiding the excessive action of running the complete test suite, thus optimizing the balance between quality assurance and testing efficiency.
2Productivity
If a subset of test cases is selected for testing in the production environment, then the testing efficiency is improved, but the ability to detect bugs and performance issues deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring production environment parameters and using this information to dynamically select relevant test cases. The system learns from production data and adjusts the test case selection to ensure that the most critical bugs and performance issues are detected, maintaining high reliability while improving testing efficiency through targeted subset selection.
Solution Approach 2:
The patent changes the parameters of test case selection by using signature files that encode specific production environment characteristics. Instead of selecting test cases based on fixed criteria, the system dynamically adjusts the selection parameters based on actual production conditions, ensuring that the subset of test cases executed is optimally suited for detecting relevant bugs and performance issues in that specific environment.
3Adaptability or versatility
If test cases are customized for the production environment, then the applicability and relevance of testing is improved, but the complexity of test case management increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing signature files for all test cases during the test suite creation phase. These signature files contain pre-analyzed characteristics and applicability criteria. When testing in the production environment, the system simply matches current production parameters against these pre-computed signatures, avoiding the need for complex real-time customization and significantly reducing test case management complexity while maintaining high adaptability.
Data Source
AI summary
Disclosed is a system and method for real-time identification of anomalous behavior in a software program. The system is configured to generate a set of signatures files corresponding to a set of test cases. In one embodiment, each signature file is configured to maintain trace data generated after running the one or more test cases on the software program in a staging environment. Further, the system may capture real-time trace data from the software program, wherein the software program is deployed in a production environment. Further, the system may analyze the real-time trace data to identify a subset of signature files, from the set of signature files, applicable to the real-time trace data. Further, the system may compare the real-time trace data with the sub set of signature files to identify anomalous behavior in the software program.