Enhancing Software Testing with AI and ML Techniques

AI and ML in Software testing

In this course, we'll explore various ways of using AI in software testing, including test case generation, test exec

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Last Updated: 2024-12-19 08:12:40 English Course Type: Self Learning

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

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.

Good knowledge or experience on Software testing is required. No programming experience required

Test engineers, Test leads

Section 1 : Introduction 1 Lectures 00:03:01
Lecture 1 :
Introduction Preview
Section 2 : Part1 - Test case generation 9 Lectures 00:28:54
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 3 Lectures 00:05:06
Lecture 1 :
Test selection using AI
Lecture 2 :
Test prioritization
Lecture 3 :
Test execution
Section 4 : Part3 - Predictive analysis 1 Lectures 00:02:03
Lecture 1 :
Predictive analysis
Section 5 : Part4 - Test automation 2 Lectures 00:05:15
Lecture 1 :
Optimize testing process
Lecture 2 :
Optimize test case
Section 6 : Part5 - AI for Performance testing 2 Lectures 00:04:35
Lecture 1 :
AI for performance testing
Lecture 2 :
More on predictive analytics
Section 7 : Conclusion 1 Lectures 00:01:55
Lecture 1 :
Conclusion

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