ML Test Suite Generation for Code Module Edge Cases
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Solution Overview
Problem
Existing methods for generating test suites for code modules are inefficient and do not adequately address edge cases, leading to unreliable software applications.
Innovation Solution
Utilizing machine learning models to generate test suites based on specifications and edge case scenarios, employing chain of thought reasoning and multi-prompt processes to create comprehensive test suites that include unit, integration, system, and regression tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to generate test suites, then the process is simple and manual, but the test coverage is insufficient and edge cases are missed
Solution Approach 1:
The patent introduces machine learning models as an intermediary between the code module and the test suite generation process. The ML model analyzes the code module, understands its functionality and edge cases, and generates comprehensive test cases automatically, thereby improving test coverage without requiring manual intervention for each test case
Solution Approach 2:
The patent replaces the manual mechanical process of test suite creation with an automated machine learning-based system. Instead of manually designing and writing test cases, the ML model automatically generates test suites by analyzing code patterns, identifying edge cases, and producing test scenarios, thus improving both coverage and efficiency
2Productivity
If manual test suite generation is used, then the process is controllable and understandable, but it is time-consuming and inefficient
Solution Approach 1:
The patent implements a self-service test generation system where the machine learning model autonomously analyzes code modules and generates appropriate test suites without requiring manual guidance for each case. The system automatically identifies test scenarios, including edge cases, and produces executable test code, thereby dramatically improving productivity and reducing the time investment required
3Reliability
If comprehensive test suites including edge cases are generated, then software reliability improves, but the complexity of test generation increases
Solution Approach 1:
The patent changes the parameters of the test generation process by using machine learning models that can adjust their analysis depth and test generation strategies based on the complexity of the code module. The ML model dynamically determines which edge cases to prioritize and how thoroughly to test different scenarios, thereby improving software reliability while managing the complexity of the generation system through adaptive parameter adjustment
Data Source
AI summary
A system accesses a code module that includes one or more units of code and instructs a machine learning model to generate a test suite based on a specification for the code module. The test suite includes tests for testing the code module to verify that one or more units of code successfully execute in accordance with the specification. The specification includes preconditions that precede successful execution of the one or more units of code and postconditions that exist following successful execution of the one or more units of code. The machine learning model generates the test suite. The system receives the test suite from the machine learning model. The system stores and/or transmits the test suite for use in testing the code module.