Regression Testing Framework Using Unique Payload Extraction
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Solution Overview
Problem
New software released into production environments often generates errors due to untested scenarios and interactions with other components, which are not replicated in testing environments, leading to critical issues that are difficult to detect and resolve.
Innovation Solution
A testing system that includes a payload processor to identify unique scenarios in the production environment, generate payloads, and a unique payload identification module to reduce millions of payloads to a manageable number, along with a data modification module to anonymize data, and a validation module to compare expected and actual results from testing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the testing system uses a limited set of predefined test scenarios, then the testing process is simple and manageable, but the software may not be tested with all possible scenarios that exist in the production environment
Solution Approach 1:
The system captures and stores production payloads in advance before software release. These real production scenarios are archived and later used to create regression test suites, ensuring that actual production conditions are preserved and can be used for testing without adding complexity to the testing process itself.
Solution Approach 2:
The system creates copies of real production payloads and scenarios. Instead of directly using production data for testing, the system captures production payloads, anonymizes them, and creates test duplicates that faithfully reproduce production conditions. This allows comprehensive scenario coverage while maintaining testing system manageability.
2Reliability
If the testing system captures and processes all production payloads, then all unique scenarios are tested, but the volume of data to be processed becomes unmanageable
Solution Approach 1:
The system extracts only the essential and unique elements from production payloads. The unique payload identification module analyzes captured payloads and extracts only those scenarios that are truly unique and representative, discarding redundant duplicates. This extraction process reduces data volume while preserving all critical testing scenarios.
Solution Approach 2:
The system changes parameters by anonymizing production data - replacing sensitive identifiers with generic placeholders. This transformation maintains the structural and behavioral characteristics of production scenarios while reducing data complexity and enabling efficient processing. The anonymization process allows the system to handle large volumes of production data without being overwhelmed by sensitive detail.
3Reliability
If the testing system uses real production data, then realistic scenarios are tested, but sensitive user data and credentials are exposed
Solution Approach 1:
The system creates anonymized copies of production data instead of using real data directly. Test payloads are duplicated from production but with sensitive information replaced by generic identifiers. This copying approach maintains scenario realism while eliminating security risks associated with exposing actual user data and credentials.
Solution Approach 2:
The system introduces an anonymization layer as an intermediary between production data and testing processes. The data modification module acts as a mediator that transforms sensitive production data into safe test data, preserving operational characteristics while removing harmful elements. This intermediary process ensures that no sensitive information leaks to testing environments.
4Productivity
If software is released quickly to production, then productivity is high, but errors may occur due to insufficient testing
Solution Approach 1:
The system performs preliminary capture of production payloads before software release. By continuously capturing and archiving real production scenarios in advance, the system builds a repository of test data that can be immediately used for regression testing when new software is released. This preliminary action enables rapid release cycles without sacrificing testing thoroughness.
Solution Approach 2:
The system implements automated regression testing that provides immediate feedback on new software releases. The regression test suite, built from captured production payloads, automatically executes against new software versions and provides rapid feedback on whether changes introduce errors. This feedback mechanism maintains high release speed while detecting errors that would occur in production conditions.
Data Source
AI summary
Systems, methods, and computer program products for testing new software are provided. Multiple payloads that correspond to scenarios in a production computing environment are identified. From the multiple payloads unique payloads are identified. User data that corresponds to the unique payloads is created. A first testing environment conducts a test using software components in the production environment, the unique payloads, and the user data to generate expected results. A second testing environment conducts a test using new software that replaces at least one of the software components in the production environment, the unique payloads, and the user data, to generate actual results. The one or more attributes in the expected results are compared to the one or more attributes in the actual results to determine if the new software causes an error.