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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

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