AI Assistant Updating Test Cases
A quality assurance team struggled to keep test cases relevant as the product grew. Traditional methods couldn't match the fast development cycles, risking outdated coverage and quality. We developed an internal AI-powered tool to optimize and improve the test case management process for our clients. Through Slack integration, we reduced update time from hours to minutes and ensured full coverage for over 2000 tests.
The tool has evolved from an internal project into a scalable out-of-the-box AI product that can help other companies facing similar challenges in maintaining large test suites while keeping pace with rapid development cycles.
Industry:
Automation processes
Services:
AI & ML, Big Data & Analytics, QA as a Service
Location:
US
Challenge
A software development team faced several challenges with their test case management:
Managing and updating a large volume of test cases (800+) efficiently;
Reducing the manual effort required for test case maintenance;
Eliminating human error in test case updates;
Speeding up the release process;
Keeping both manual test cases and automated BDD scenarios synchronized with new requirements;
Reducing time to market while maintaining quality.
Solution
We developed an AI-powered testing assistant that upgrades test case management process.
Backend Processing
Single AI assistant implementation with sequential processing stages
Custom vector storage solutions for improved accuracy
Automated identification of affected test cases
Test Analysis
Smart mapping of requirement changes to existing scenarios
Automated generation of test case update recommendations
Support for both manual tests and BDD/Gherkin format
Security Implementation
Secure handling of sensitive test data
Environment variable protection
Configurable access controls
Full demo of AI assistant in action you can see follow this link on YouTube.
Technologies Used
Our tech stack is a curated selection of tools and frameworks. Each one addresses a specific aspect of AI-powered test case management. They ensure seamless integration and high performance.
The workflow
A step-by-step implementation process designed for efficient AI-powered test case management - from initial setup to continuous monitoring, ensuring accurate analysis and updates for over 2000 test cases.
Define objectives and requirements
Define test case analysis requirements and performance targets for 2000+ existing test cases.
Select AI models
Configure three AI assistants for analysis, refinement, and summarization phases.
Customize system prompts and instructions
Design specialized prompts and instructions for each AI assistant to handle test cases and BDD scenarios.
Set temperature and Top_p
Optimize AI settings for precise and accurate test case processing results.
Integrate multiple AI assistants
Set up workflow between AI assistants for seamless processing from analysis to final output.
Implement optimized vector storage
Configure vector storage for efficient test case matching and retrieval.
Data privacy and security
Implement secure environment variables and configuration management for sensitive data.
Slack integration
Deploy an AI testing bot to Slack workspace channel using a single command that triggers sequential backend processing for test case analysis and updates.
Monitor and optimize
Track performance metrics and continuously improve system accuracy and efficiency.
About the team
The implementation team consisted of:
Team composition
QA engineers
1
AI specialists
1
DevOps engineer
1
Full-stack developer
1
Impact
This AI solution improved the client's test case management. It saved time, increased accuracy, and reduced errors. The system handled manual tests and automated BDD scenarios, meeting modern testing needs.
Time & efficiency
The system runs over 2000 test cases at once. This speeds up updates and removes bottlenecks in release cycles.
Quality & accuracy
We've nearly eradicated human error in test upkeep. The AI accurately spots impacted tests, ensuring vital updates aren't overlooked. This thorough approach keeps tests in sync with new features.
Process & resources
QA teams now emphasize strategic testing over manual updates. An automated system manages complex analyses and keeps manual tests and BDD/Gherkin scenarios. This shift frees engineers to focus on more valuable tasks.
Team collaboration
The close work between QA engineers and AI experts has made testing faster. Now, teams can quickly adapt to changes without losing quality or coverage.
