Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Artificial Intelligence in Software Testing
- Examination of artificial intelligence capabilities within testing and quality assurance functions
- Classification of artificial intelligence tools utilized in contemporary test workflows
- Assessment of advantages and potential risks associated with AI-driven quality engineering practices for government systems
Utilization of Large Language Models for Test Case Generation
- Application of prompt engineering techniques to develop unit and functional test cases
- Development of parameterized and data-driven testing templates
- Transformation of user stories and system requirements into automated test scripts
Artificial Intelligence Applications in Exploratory and Edge Case Testing
- Identification of untested code branches or logical conditions through artificial intelligence analysis
- Simulation of infrequent or atypical operational scenarios
- Implementation of risk-based strategies for test case generation
Automated User Interface and Regression Testing
- Deployment of artificial intelligence platforms, such as Testim or mabl, to facilitate user interface test creation
- Maintenance of test stability through the use of self-healing locators
- Utilization of AI to conduct regression impact analysis following code modifications
Failure Analysis and Test Optimization Strategies
- Categorization of test failures using large language or machine learning models
- Reduction of inconsistent test results and alert fatigue
- Prioritization of test execution sequences based on historical performance data
Integration with Continuous Integration and Continuous Deployment Pipelines
- Incorporation of AI-driven test generation within Jenkins, GitHub Actions, or GitLab CI environments for government applications
- Validation of test quality during the pull request review process
- Management of automation rollbacks and implementation of intelligent testing gates within deployment pipelines
Emerging Trends and Responsible Implementation of AI in Quality Assurance
- Evaluation of the accuracy, reliability, and safety of AI-generated test assets for government use
- Establishment of governance frameworks and audit trails for AI-enhanced testing procedures
- Analysis of emerging trends in AI-QA platforms and intelligent system observability
Summary and Next Steps
Requirements
- Demonstrated proficiency in software quality assurance, test strategy development, and automated testing processes
- Working knowledge of industry-standard testing frameworks, including JUnit, PyTest, and Selenium
- Foundational understanding of continuous integration and deployment (CI/CD) pipelines within DevOps infrastructure
Audience
- Quality Assurance engineers
- Software Development Engineers in Test (SDETs)
- Testing professionals operating within agile or DevOps ecosystems for government programs
14 Hours
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