Transform your business with GenAI-infused apps.
Protect it with Gen AI App Testing and Quality Engineering.

Testing a Generative AI (Gen AI) application involves several steps and considerations to ensure the model’s accuracy, robustness, and reliability.

Here are a few key gen AI app testing approaches and solutions based on the provided documents:

Automated Question Generation:  

Utilize automated solutions to generate questions from documents. This reduces the time testers need to spend on creating questions manually, enhancing efficiency in gen AI infused app testing.

Model Graded Evaluation:  

Implement a model graded evaluation system to benchmark the model’s accuracy early in the development process. This helps in identifying issues at an early stage.

Globalization Context and Bias Detection:  

Develop solutions to test the application for globalization context, remove bias, and detect hallucinations. This ensures that the model performs well across different languages and cultural contexts.

Prompt Generation and Translation:  

Automate the generation and translation of prompts to test the model’s ability to handle various inputs and produce accurate outputs.

Requirement Traceability:  

Implement requirement traceability to identify test cases impacted by requirement changes. This helps in maintaining the relevance and accuracy of test cases as the application evolves.

Self-Healing Test Scripts:  

Use tools like Healinium for web application self-healing. These tools can automatically scan web pages, identify UI changes, and update test scripts, accordingly, enhancing gen AI app quality engineering for more resilient testing.

Synthetic Data Generation:  

Generate synthetic data for selected fields to test the model’s performance with different data sets. This includes creating data in several tables and rule-based data.

By following these gen AI app testing approaches, you can effectively test a Gen AI application and ensure it meets the desired quality standards.

Generative AI can significantly enhance testing activities through various innovative approaches and solutions.

Here are a few key steps in which Generative AI can be utilized to improve gen AI app testing:

• Automated Test Script Generation
• Model Graded Evaluation
•  Automated Question Generation
•  Globalization Context and Bias Detection
• Prompt Generation and Translation
•  Self-Healing Test Scripts

By leveraging these approaches, gen AI infused app testing activities can be made more efficient, accurate, and robust, ultimately leading to higher quality software products.

Gen AI improves application quality engineering processes in several ways:

Test Case and Script Generation: 

Gen AI app testing can automatically generate test cases and scripts from user stories, functional specifications, and design documents. This includes generating positive, negative, and edge test cases in multiple languages.

Self-Healing:  

Gen AI enhances self-healing capabilities by identifying changes in objects and other technical elements in the application under test and automatically correcting the test scripts. This reduces the maintenance effort required for test scripts.

Defect Analysis and Triaging:  

Gen AI can perform defect analysis using NLP and ML, reducing duplicate defects and improving the efficiency of defect triaging and root cause analysis (RCA).

Requirement Refinement and Test Strategy Creation:  

Gen AI helps in refining requirements, creating test strategies, and generating test cases from natural language requirements or user stories. This ensures that the requirements are clear, complete, and testable.

Performance Testing:

Gen AI app testing assists in performance testing by planning and executing tests, detecting bottlenecks, and optimizing load scripts automatically. This ensures that the application can handle the expected load efficiently.

Model-Based Testing:  

Gen AI can create models from requirements and generate process models into test cases and scripts. This approach helps in visualizing and enhancing the models manually before generating comprehensive test scripts.

API Script Generation:  

Gen AI can generate API test scripts from requirements or reverse engineer them from API logs, ensuring comprehensive testing of API endpoints.

Knowledge Management:  

Gen AI supports knowledge management for onboarding new project members by providing a conversational assistant and RAG (retrieval-augmented generation) capabilities, which help in quickly bringing new team members up to speed.

Overall, gen AI infused app testing enhances the efficiency, accuracy, and coverage of quality engineering processes, leading to improved application quality and reduced time-to-market.

Gen AI in the context of application quality engineering (QE) refers to the use of generative artificial intelligence to enhance various aspects of the testing and quality assurance processes. This includes automating tasks, improving efficiency, and ensuring higher quality in genAI app testing.

Here are some specific applications of GenAI quality engineering:

• Requirement Analysis and Test Planning
• Test Case and Script Generation
• Test Execution and Optimization
• Data Management
• Defect Management and Analysis
• Knowledge Management and Onboarding
• Model Engineering and AI Assurance

These applications demonstrate how gen AI app testing can significantly enhance the efficiency, accuracy, and overall quality of genAI quality engineering processes.