Context-Based Test Suite Generation for Code Dependency Mapping
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Solution Overview
Problem
In complex computing environments, developers often unintentionally introduce or expose defects due to code dependencies, and existing testing methods require manual effort and limited understanding of feature interactions, leading to incomplete test coverage and 'testing blind spots'.
Innovation Solution
A testing system that uses machine learning algorithms to assess code changes, identify dependencies, generate context-based test suites, and execute tests across the entire system, automatically mapping feature interactions and selecting relevant test cases to ensure comprehensive validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test suite management and execution is used, then developers can confirm feature functionality, but the testing burden falls on developers and test coverage is limited by manual effort
Solution Approach 1:
The system enables self-service testing by automatically generating test suites based on code changes and their dependencies. The testing system independently identifies affected features, selects relevant test cases, and executes tests without requiring developer intervention in test suite compilation, thereby improving both test coverage and efficiency
Solution Approach 2:
The patent replaces the mechanical manual process of test suite management with an automated computational system. Machine learning algorithms analyze code dependencies, map features to test cases, and automatically compile and execute test suites, substituting human manual effort with automated intelligence
2Reliability
If developers manually map test cases to features, then some testing can be performed, but testing blind spots exist due to limited understanding of feature interactions
Solution Approach 1:
The system introduces an intermediary automated testing system that sits between code changes and test execution. This intermediary analyzes code dependencies, automatically maps features to test cases using machine learning, and identifies test gaps that manual processes would miss, thereby improving test completeness without requiring deep system understanding from developers
Solution Approach 2:
The patent replaces the limited human cognitive process of mapping test cases with an automated machine learning system. The system computationally analyzes code dependencies, feature interactions, and test case relevance, achieving comprehensive test coverage that exceeds human capability to understand and map all interactions
3Productivity
If DevOps and Continuous Testing are used to automatically execute test suites, then testing burden is reduced, but test suite compilation still requires manual effort and has limited scope
Solution Approach 1:
The system dynamically generates test suites based on the specific code changes being made. Rather than using static, pre-defined test suites, the system adapts the test scope and content to match the actual changes and their dependencies, ensuring test suite relevance is maintained as the codebase evolves
Solution Approach 2:
The system performs preliminary analysis of code changes and their dependencies before test suite execution. By pre-identifying affected features and selecting relevant test cases in advance, the system ensures that the automated test execution is both efficient and highly relevant to the specific changes being tested
Data Source
AI summary
A system comprising processing circuitry a memory storing instructions that cause the system to detect a code change to source code included in a code repository, identify a relationship between the code change and an associated product feature, determine one or more dependent product features impacted by the code change, select a set of test cases including a subset of test cases related to the associated product feature and a subset of test cases related to the one or more dependent product features, execute the set of test cases, and update the code-to-feature mapping using results of executing the set of test case.