Regression Test Failure Identification via Smart Code Instrumentation
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Solution Overview
Problem
Regression testing in container environments is challenging due to complex business logic and the lack of debugger tools, making it difficult to identify the root cause of failures in newly built images, especially in cloud-based applications.
Innovation Solution
A smart code instrumentation technique that compares base and new code, inserts debug code into corresponding source files, builds images for containers, and compares debugging outputs to identify and correct regression test failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional regression testing is used in container environments, then resource usage is reduced through containerization, but the ability to identify root cause of failures deteriorates due to lack of debugger tools and complex business logic
Solution Approach 1:
The system performs preliminary code comparison between base code and new code before executing regression tests, inserting debug code at relevant locations in advance. This preliminary analysis of code differences enables targeted debugging without requiring full debugger toolchains in the container runtime environment.
Solution Approach 2:
The patent introduces an intermediary code comparison and analysis system that bridges the gap between containerized regression testing and root cause identification. This intermediary layer compares code versions, identifies differences, and guides the testing process to focus on affected areas, compensating for the lack of traditional debugger tools within containers.
2Measurement precision
If code comparison and debug code insertion is performed, then debugging accuracy is improved, but processing time and complexity increase
Solution Approach 1:
Instead of applying uniform debugging instrumentation across entire codebases, the system applies debug code insertion selectively only to regions identified through code comparison as being modified between base and new versions. This localized approach maintains high debugging accuracy while minimizing processing overhead.
Solution Approach 2:
The patent performs partial code comparison and debug insertion rather than comprehensive full-code analysis. By focusing only on the subset of code that has changed and is relevant to the regression test failures, the system achieves sufficient debugging accuracy without the prohibitive time cost of analyzing entire codebases.
Data Source
AI summary
Examples described herein provide a computer-implemented method for identifying regression test failures that includes comparing a base code to a new code to locate an updated aspect of a program. The method further includes inserting debug code into corresponding source files for each of the base code and the new code for the updated aspect. The method further includes building a first image for the base code and a second image for the new code, the first and second images running in respective first and second containers. The method further includes comparing debugging outputs from a regression test of the respective first and second containers to identify a regression test failure. The method further includes implementing a corrective action to correct the regression test failure.