Touchless Test Prioritization for Large Regression Suites
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Solution Overview
Problem
Existing software testing methodologies face challenges in efficiently managing and automating large regression test suites, which are time-consuming and labor-intensive, and often fail to maximize defect yield while maintaining code quality and coverage.
Innovation Solution
A touchless automated testing platform that uses AI and machine-learning algorithms to analyze churn data, usage patterns, and failure patterns to prioritize and sequence test cases, generating automated testing scripts and executing them to maximize defect yield and improve code quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing of large regression test suites is performed, then testing coverage can be achieved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system automatically performs test case selection, sequencing, and execution without manual intervention. The intelligent agent autonomously analyzes code changes, selects relevant test cases, determines execution sequences, and generates test reports, enabling the testing process to serve itself and eliminate manual labor while maintaining comprehensive coverage
Solution Approach 2:
The patent replaces manual mechanical testing operations with an intelligent automated system that uses machine learning algorithms and AI agents to analyze code changes, select test cases, and execute testing sequences, substituting human effort with intelligent automation that operates faster and more efficiently
2Reliability
If all test cases in a large regression test suite are executed, then comprehensive defect detection is achieved, but execution time and computational resources increase
Solution Approach 1:
The system extracts and selects only the most relevant test cases from the complete regression test suite based on code change analysis and impact assessment. The intelligent agent identifies and extracts high-priority test cases that are most likely to detect defects in changed code areas, eliminating unnecessary test executions while maintaining effective defect detection
Solution Approach 2:
The patent applies different selection and sequencing strategies to different portions of the test suite based on local code change characteristics. The system analyzes specific modules, classes, or functions that have changed and applies targeted test case selection criteria to those local areas, rather than uniformly processing the entire test suite, thereby optimizing resource allocation and execution efficiency
3Productivity
If test cases are prioritized and sequenced based on code changes, then testing efficiency improves, but complexity of test management increases
Solution Approach 1:
The system introduces an intelligent agent as an intermediary between code change detection and test case execution. This agent automatically analyzes code changes, determines test case priorities, and sequences test executions based on impact analysis, thereby managing the complexity internally and presenting a simplified interface to users while maintaining high testing efficiency
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors test execution results, code change patterns, and defect detection outcomes to dynamically adjust test case selection and sequencing strategies. This feedback loop enables the system to learn from past performance and automatically optimize test management without increasing user burden, balancing efficiency gains with manageable complexity
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.