Automation Testing
Without AI & With AI Integration
Without AI
01
Test Automation Strategy
Objective: Develop an automation strategy based on the project’s needs, budget, and timeline.
Steps: Identify which test cases to automate based on frequency and importance (e.g., smoke tests, critical paths).
Select the best-fit automation tools (e.g., Selenium, Appium) based on project requirements and skill sets.
Define the approach for automation (e.g., keyword-driven, data-driven).
02
Automation Framework Design
Objective: Set up a robust framework that allows for efficient and maintainable automation.
Steps: Choose the test framework structure: modular, data-driven, or keyword-driven.
Integrate with CI/CD pipelines for seamless integration with build/deployment processes.
Ensure reusability of test scripts to accommodate future changes and reduce maintenance.
03
Test Script Development & Execution
Objective: Develop and execute automated test scripts for functional and regression tests.
Steps: Write automated test scripts for the selected test cases, ensuring that they can run on different browsers and platforms.
Execute these scripts regularly, either manually triggered or automatically as part of CI/CD pipelines.
Log the results of the automated tests, comparing actual outcomes with expected ones.
04
Maintenance of Test Scripts
Objective: Regularly update automation scripts as the website evolves and changes occur.
Steps: As the website undergoes updates, modify the test scripts to reflect these changes (e.g., UI updates, new workflows).
Ensure that the test scripts remain functional and accurate, reducing false positives.
Periodically refactor scripts to improve efficiency and maintainability.
05
Reporting
Objective: Provide detailed reports that summarize the outcomes of automated tests.
Steps: Generate and review reports to identify failed test cases and their root causes.
Provide logs, screenshots, and videos for failed test cases to help developers address issues.
Provide metrics, including test execution time, pass/fail rates, and error patterns.
With AI
01
AI-Driven Test Strategy
Objective: Use AI to optimize the selection of tests that need to be automated based on user behavior data and previous issues.
Steps: AI tools analyze past data to predict which parts of the application have the highest likelihood of failure.
The AI suggests the most critical tests to automate, ensuring efficient use of resources.
02
AI-Enhanced Test Script Creation
Objective: Automate test script creation using AI to generate tests based on UI changes, user behavior, and application logic.
Steps: AI tools automatically generate test scripts as the website evolves, reducing the need for manual script writing.
AI tools can also suggest additional edge cases or scenarios based on past failures or common user interactions.
03
Smart Test Execution
Objective: Use AI to adapt test execution paths and optimize which tests to run based on historical failure rates.
Steps: AI dynamically prioritizes tests based on predicted risk levels, running the most critical tests first.
AI minimizes the execution of redundant tests, improving testing efficiency.
04
Adaptive Test Maintenance
Objective: Use AI to automatically maintain and update test scripts to adapt to changes in the website’s UI and features.
Steps: As the website’s interface or features change, AI tools adjust test scripts automatically without manual intervention.
AI systems detect changes and update scripts or suggest required updates to ensure comprehensive coverage.
05
AI Reporting & Insights
Objective: Use AI-driven reporting tools to generate actionable insights that inform development and testing teams about performance and defects.
Steps: AI generates reports that highlight patterns in defects, such as high-failure areas or frequent code changes.
AI-generated reports provide deeper insights into the causes of test failures, offering suggestions for improvement or areas to focus on in the next round of testing.