1.Efficiency and Speed:
- Automation of Repetitive Tasks: AI can automate repetitive and mundane tasks such as regression testing, freeing up human testers to focus on more complex scenarios. ( This comes under Test Automation)
2.Accuracy:
- Reduction of Human Error: By automating tests, AI minimizes the risk of human error, leading to more consistent and reliable results. ( This comes under Test Automation, still it may not be 100 % error free)
- Precision in Test Execution: AI can execute tests with high precision, ensuring that all test cases are covered accurately. ( This comes under Test Automation)
3.Cost-Effective:
- Long-Term Savings: While the initial investment in AI tools and training can be high, the long-term savings from reduced manual testing efforts can be substantial.
- Resource Optimization: AI can optimize the use of resources by identifying and prioritizing the most critical test cases.
4.Scalability:
- Handling Large Data Volumes: AI can process and analyze large volumes of data quickly, making it suitable for testing applications with extensive datasets.
- Complex Test Scenarios: AI can manage complex test scenarios that would be challenging for human testers to handle manually. (provided the automation script is coded accurately)
5.Predictive Analysis:
- Early Bug Detection: AI can analyze historical data to predict potential issues and bugs before they occur, allowing for proactive problem-solving.
- Risk Assessment: AI can assess the risk associated with different parts of the application, helping prioritize testing efforts.
6.Improved Test Coverage:
- Automatic Test Generation: AI can automatically generate test cases based on the application’s requirements and user behavior, ensuring comprehensive test coverage. ( esp. LLM Model if prompts are correct)