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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Exploring the capabilities of AI in testing and QA.
- Surveying the types of AI tools integrated into modern test workflows.
- Assessing the advantages and potential risks of AI-driven quality engineering.
Utilising LLMs for Test Case Creation
- Applying prompt engineering techniques to generate unit and functional tests.
- Developing parameterised and data-driven test templates.
- Translating user stories and business requirements into executable test scripts.
AI-Driven Exploratory and Edge Case Testing
- Detecting untested code branches or conditions with the aid of AI.
- Replicating rare or irregular user interaction scenarios.
- Implementing risk-based strategies for test generation.
Automation of UI and Regression Testing
- Employing AI platforms like Testim or mabl to build UI tests.
- Ensuring UI test stability via self-healing selector mechanisms.
- Conducting AI-assisted regression impact analysis following code modifications.
Failure Analysis and Test Efficiency
- Aggregating test failures using LLM or ML models.
- Minimising flaky test executions and reducing alert noise.
- Optimising test execution order based on historical data insights.
Integrating with CI/CD Pipelines
- Incorporating AI test generation into Jenkins, GitHub Actions, or GitLab CI.
- Assessing test quality during the pull request process.
- Implementing automated rollbacks and intelligent test gating within pipelines.
Emerging Trends and Ethical AI Use in QA
- Reviewing the accuracy and safety of AI-generated test cases.
- Establishing governance frameworks and audit trails for AI-enhanced testing.
- Observing trends in AI-QA platforms and intelligent observability solutions.
Wrap-Up and Future Actions
Requirements
- Prior experience in software testing, test planning, or QA automation.
- Working knowledge of popular testing frameworks such as JUnit, PyTest, or Selenium.
- Fundamental understanding of CI/CD pipelines and DevOps environments.
Target Audience
- Quality Assurance Engineers.
- Software Development Engineers in Test (SDETs).
- Software testers operating within agile or DevOps frameworks.
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
