AI Test Case Selection Using Ticket Summaries and Relationship Counters
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Solution Overview
Problem
Existing software development tools lack efficient integration between project management and test case management, leading to inefficient methods for organizing and finding relevant test cases, which hampers the testing process.
Innovation Solution
Implementing a test case recommendation module that utilizes a large language model to summarize test cases and tickets, and employs relationship counters to enhance the recommendation of relevant test cases based on historical data and connections within a test case library.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword searching in spreadsheets is used to find test cases, then test case management is simple and requires minimal integration, but the efficiency of finding relevant test cases deteriorates
Solution Approach 1:
The patent merges project management tools with test case management tools into an integrated system. The test case recommendation module is embedded within the project management interface, allowing developers to access recommended test cases directly from ticket views without switching between separate spreadsheet-based test case management systems. This integration combines the simplicity of unified tool usage with automated AI-driven recommendations to improve efficiency.
Solution Approach 2:
The system employs AI agents that automatically analyze ticket descriptions, requirements, and historical data to generate and recommend relevant test cases without manual intervention. The test case recommendation module autonomously processes project management data, identifies applicable test cases from the test case library, and presents recommendations to developers, eliminating the need for manual keyword searching in spreadsheets.
2Device complexity
If manual organization of test cases is used, then integration complexity between tools is reduced, but the reliability of test case selection deteriorates
Solution Approach 1:
The patent introduces an intermediary test case recommendation module that acts as a bridge between project management tools and test case management functionality. This module receives data from project management systems, processes it through AI analysis, and returns recommended test cases. The intermediary handles the complexity of integration internally while presenting a simple interface to users, thereby maintaining low perceived complexity while achieving high reliability through automated analysis.
Solution Approach 2:
The system implements feedback loops where the test case recommendation module continuously learns from project management data, test execution results, and user interactions. AI agents analyze the effectiveness of recommended test cases and adjust future recommendations accordingly. This feedback mechanism improves the reliability of test case selection over time while the integrated architecture manages integration complexity through standardized interfaces.
3Productivity
If AI-based test case recommendation is implemented, then the efficiency of test case selection improves, but the device complexity increases
Solution Approach 1:
The patent segments the test case management system into distinct modular components: project management module, test case library, AI analysis engine, and recommendation module. Each component performs a specific function and communicates through defined interfaces. This segmentation allows the AI-based recommendation functionality to be added without overwhelming complexity, as each module can be developed, maintained, and scaled independently while contributing to the overall efficiency improvement.
Data Source
AI summary
A test case management tool integrated in a project management tool can include user interface elements supported by backend databases and a test case library database. The user interface elements can include an option for a user to request an automatic artificial intelligence-based recommendation for a ticket project stored in the project management tool. The automatic test case selection includes LLM summarization of the test cases in the test case library database, and semantic searching of ticket data against the summaries. Results can be improved by generating relationship counters, based on the relationships identified in the test case library database, scoring each test case, based on the relationship counters, and selecting higher scored test cases.