🤖 Day 13: Develop a testing approach and become an AI in testing champion!

📆 30 Days of Testing
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Hi All

Please find the below proposal idea.

AI in Testing Champion Proposal

1. Introduction

Currently, our mobile app testing for transportation application primarily relies on manual processes, Swift, XCUITest for UI test automation, Azure Wiki for test case design, and Azure Pipeline for CI/CD and reporting. However, with a two-week release cycle, our testing process faces challenges in efficiency and coverage.

2. Challenges

  • Test Data Management: Generating diverse test data is time-consuming, with approximately 30% of testing time dedicated to data management activities [1].
  • Test Design: Creating comprehensive test cases requires extensive effort, with approximately 14% of the total project effort spent on designing test cases [2].
  • Test Execution: Manual execution of UI tests is labor-intensive and can take up to 75% more time than automated testing [3].
  • Managing Defects: Identifying and resolving defects within tight release cycles is challenging, with costs ranging from $25 to $1,500 per defect [3].
  • Test Reporting: Compiling meaningful test reports is resource-intensive and can take up to 55% of total testing time [3].

3. AI Integration

Incorporating AI at every stage of the testing process can revolutionie our approach:

  • Generative AI for Efficient Test Data Generation and Management: Generative AI can create synthetic data resembling real-world scenarios, reducing storage requirements and ensuring fresh data for continuous testing [4].
  • AI-driven Test Execution Optimisation: AI algorithms can optimise test case selection and execution order based on past executions and user interactions, leading to reduced manual effort, improved coverage, and faster time-to-market [5].

4. Area of Focus: Test Execution Optimisation

How AI Will Be Used:

  • Implement AI-driven test automation using Swift and XCUITest with machine learning capabilities.
  • AI algorithms will optimise test case selection and execution order based on past test executions and user interactions.
  • Predictive analysis will identify high-risk areas and prioritise test execution, enabling proactive testing efforts.
  • Continuous monitoring will detect anomalies in test results, triggering alerts for immediate investigation and resolution.

Impact:

  • Manual effort reduction for test execution by up to 50%.
  • Improved test coverage and reliability, leading to enhanced software quality and reduced defect leakage.
  • Faster time-to-market due to expedited test execution and early defect detection.

5. Mitigating AI Risks

  • Rigorous validation of AI models to ensure accuracy and reliability.
  • Regular monitoring and refinement of AI algorithms to adapt to changing testing needs.
  • Training and up-skilling testers to understand AI-driven testing methodologies and address potential challenges.

6. Conclusion

AI integration in our testing process presents a significant opportunity to enhance efficiency, effectiveness, and software quality. By championing AI in testing, we can unlock its full potential and drive innovation in software quality assurance.

References:

  1. Survey: [How to Manage Test Data in Your Test Automation Project]
  2. World Quality Report 2020: [https://vates.com/test-driven-development-tdd-building-quality-software-through-testing/]
  3. Maximising Testing Efficiency:[https://www.nousinfosystems.com/insights/blog/how-test-data-managem]
  4. Generative AI for Efficient Test Data Generation and Management: [Generative AI for Efficient Test Data Generation and Management | LambdaTest]
  5. Ultimate Guide to Test Data Management: [https://magedata.ai/ultimate-guide-to-test-data-management/]

Other References:
i. https://theqalead.com/tools/best-test-data-management-tools/
ii.https://testautomationforum.com/test-data-management-using-ai-powered-synthetic-data-generators/

Thank you

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