AI in the area of Quality Assurance
Application of Artificial Intelligence
Artificial Intelligence (AI) is revolutionizing the world of Quality Assurance (QA). By automating testing, AI increases the efficiency and accuracy of QA processes, allowing teams to focus on strategic quality aspects. AI analyzes data, detects patterns, and predicts errors, contributing to faster and more effective software quality assurance. In this presentation, we will see how AI is changing the face of QA, from automation to error analysis and future trends.
Systematic project approach – Implementation phases
- Defining goals and expectations for AI solution implementation
- Audit/verification of current processes and technologies
- Matching AI tool solutions
- Implementation planning
- Implementation, monitoring, and tool adjustment
- Optimization and scaling
- Managing AI culture
- Ensuring compliance and security
Test Management and Automation using the proprietary solution Power Farm
PF is a modern platform for managing, standardizing, and centralizing services for creating automated tests and process automation.
Key features of PF:
- Experience: Based on years of experience in running testing projects, mainly in the banking sector.
- Efficiency: Methodology for improving the processes of creating and maintaining automated tests that maximizes the use of previously created automation elements.
- Flexibility: PF does not require technological changes in existing script creation processes.
- License optimization: Effective management of the automation process allows for optimal use of existing licenses, such as HP UFT.
- No additional costs: Using OpenSource tools eliminates the need to purchase commercial solutions.
The PF platform is an ideal solution for companies looking to streamline their testing and automation processes while reducing costs and increasing efficiency.
General AI environment architecture
Key functionalities:
- Combining automation in multiple technologies and layers (end-to-end processes)
- Managing and versioning the complete code of automation across multiple environments
- Advanced task queuing and prioritization mechanisms
- Standardization in the areas of production, release, and maintenance
- Access control and a launch panel for non-technical users
- Modern, scalable platform architecture with an open API
Implementation approach
1. Consulting
- Analysis of the current state of automation in the organization
- Agreeing on the organization's goals for the project
- Agreeing on the automation operation model in the organization
- Process and tool optimization
- Preparation for implementation
2. Implementation
- Customizing the platform to the specific requirements of the organization
- Platform installation and integration with external tools
- Integration of existing content
- Training on the process and the platform
- Stabilization through the creation of automation
- Roadshow
3. Wsparcie
- Defining the scope and sequence of test automation
- Platform development through CR implementation
- Delivering new versions and functionalities
- Creating automation for client needs
- Additional training
- Platform maintenance