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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- Ways to use AI for Test case generation, Ways to use AI for Test execution, Ways to use AI for Predictive analysis, Ways to use AI for Performance testing
Course Overview
In this course, we'll explore various ways of using AI in software testing, including test case generation, test execution, predictive analytics, test automation, and performance testing.
[Part 1: Test Case Generation] One of the most significant ways of using AI in software testing is test case generation. By using machine learning algorithms to analyze requirements and specifications, AI can automatically generate test cases that cover different scenarios and edge cases that might otherwise be missed by manual testing. This process can help improve test coverage and reduce the time and effort required for manual test case creation.
[Part 2: Test Execution] AI can also be used to improve test execution by automating test execution and analysis. By using AI algorithms to identify defects and issues in real-time during test execution, we can proactively address them before they become major problems, reducing the time and resources required for defect resolution.
[Part 3: Predictive Analytics] Another way of using AI in software testing is through predictive analytics. By analyzing historical data on defects and issues, AI algorithms can predict potential issues before they occur, allowing us to proactively address them before they become major problems. This process can help improve the quality of the software and reduce the time and resources required for manual testing and defect resolution.
[Part 4: Test Automation] AI can also be used to automate various aspects of testing, such as test case selection, prioritization, and execution. By using machine learning algorithms to optimize the testing process, we can achieve maximum coverage with minimal effort, reducing the time and resources required for manual testing and increasing the efficiency of the testing process.
[Part 5: Performance Testing] Lastly, AI can be used to optimize performance testing by analyzing performance data and identifying performance issues. By using machine learning algorithms to identify patterns in performance data, we can predict potential performance issues and proactively address them before they become major problems.
Pre-requisites
- Good knowledge or experience on Software testing is required. No programming experience required
Target Audience
- Test engineers, Test leads
Curriculum 22 Lectures 00:50:49
-
Section 1 : Introduction
-
Section 2 : Part1 - Test case generation
- Lecture 1 :
- Approaches for Test case generation
- Lecture 2 :
- Sample ML Model - Part1
- Lecture 3 :
- Sample ML Model - Part2
- Lecture 4 :
- Sample ML Model - Part3
- Lecture 5 :
- NLP for requirement analysis
- Lecture 6 :
- More on dependency parsing
- Lecture 7 :
- More on sentiment analysis
- Lecture 8 :
- Genetic algorithms for test case generation
- Lecture 9 :
- Random testing for test case generation
-
Section 3 : Part2 - Test execution
- Lecture 1 :
- Test selection using AI
- Lecture 2 :
- Test prioritization
- Lecture 3 :
- Test execution
-
Section 4 : Part3 - Predictive analysis
- Lecture 1 :
- Predictive analysis
-
Section 5 : Part4 - Test automation
- Lecture 1 :
- Optimize testing process
- Lecture 2 :
- Optimize test case
-
Section 6 : Part5 - AI for Performance testing
- Lecture 1 :
- AI for performance testing
- Lecture 2 :
- More on predictive analytics
-
Section 7 : Conclusion
- Lecture 1 :
- Conclusion
Our learners work at
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