Adaptive Test Execution and Optimisation using  Artificial Neural Networks

By leveraging neural networks' ability to learn from data and make intelligent decisions, testing teams can optimise test execution, reduce redundant tests, and enhance the efficiency of their testing processes.

Introduction

The Concept of Adaptive Test Execution

Adaptive test execution refers to dynamically selecting and prioritizing test cases based on various factors such as code changes, historical test results, and risk analysis.

Artificial Neural Networks in Software Testing

This explains the structure and functioning of neural networks and how they can be trained to make predictions and optimise test execution based on input data.

Training Neural Networks for Test Execution Optimisation

Training neural networks for adaptive test execution involves feeding them with relevant training data, such as historical test results and code changes.

Leveraging Neural Networks for Test Execution Optimisation

There are techniques such as prediction-based prioritisation, test suite reduction, and adaptive resource allocation. Neural networks enable testing teams to achieve better test coverage with reduced execution time.

Benefits of Adaptive Test Execution using Neural Networks

This  highlights the benefits of adaptive test execution using neural networks, including improved test efficiency, reduced test maintenance efforts, and increased defect detection.

Future Trends in Adaptive Test Execution using Neural Networks

Future trends in adaptive test execution using neural networks, such as the integration of real-time data, the combination of multiple machine learning techniques, and the development of hybrid approaches.

Conclusion

Artificial neural networks offer a promising approach to adaptive test execution and optimisation. By leveraging the power of machine learning, testing teams can dynamically prioritise and execute test cases, leading to improved efficiency and quality in software testing.

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