How Would Software Testing Change the Future - PowerPoint PPT Presentation

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How Would Software Testing Change the Future

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Automation and AI-based approaches are often used in non-functional testing to identify and prioritize application components that may be more vulnerable to performance or security concerns. – PowerPoint PPT presentation

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Title: How Would Software Testing Change the Future


1
How Would Software Testing Change in the Future?
  • A snapshot of new software testing tendencies
  • Automation and AI-based approaches are often used
    in non-functional testing to identify and
    prioritize application components that may be
    more vulnerable to performance or security
    concerns. While cloud- based testing offers more
    scalability and flexibility, shift-left testing
    tries to identify issues early in the
    development process. Trends in automation testing
    are assisting in increasing the process's
    effectiveness, economy, and dependability. These
    developments will keep influencing how software
    is tested and used.
  • Keep in Mind These Testing Trends
  • DevOps and security testing
  • Cybersecurity testing
  • IoT testing
  • Big data testing
  • Cloud-based testing
  • Automated Testing
  • Continuous and Performance Testing
  • Artificial Intelligence (AI)
  • Mobile App testing

2
  • What role does AI play in software testing?
  • Astute test selection
  • Because AI can automatically collect and analyze
    information and evaluate test results, it can
    identify which tests are required and which are
    unnecessary.
  • Machine learning
  • Machine learning may aid software testing by
    automatically creating test cases, auditing
    existing tests for coverage, speed, and
    completeness, as well as running them.
  • Advantages of AI for software testing
  • Reduced test execution time
  • AI may shorten test execution time by refuting or
    deleting redundant tests and improving test data
    management flow by ensuring high-quality data
    reaches the test cases initially.
  • Improved test management
  • Software code develops quickly and adjusts tests
    to ensure compatibility with new source code,
    increasing the time and cost of test maintenance.
    AI can help to lessen this by employing computer
    vision bots and brilliant test selection.

3
  • Computationally pricey
  • Machine learning and deep learning are
    computationally costly to implement. Because of
    the high computing cost, several AI models built
    by Facebook and Google are commercially unviable.
  • Important Procedures for Software Testing
    Services
  • Test Automation with No Code
  • Low-code or no-code solutions provide by test
    automation technologies to increase application
    delivery speed and quality. These tools include
    functionality like recording and playback, drag
    and drop, and AI/ML technology integration.
  • RPA-led Testing
  • RPA technologies use to shorten the test cycle by
    creating reusable components and bot to assist in
    various testing phases. In addition, AI/ML and
    RPA approaches can help the software testing
    process to keep up with new technologies.
  • AI/ML Methodologies
  • AI/ML approaches enhance software development and
    quality assurance procedures, such as finding
    duplicate test cases and improving regression
    testing.
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