Software testing and quality assurance are crucial for developing reliable and high-performance software applications.
Traditional manual testing techniques are very time-consuming with inherent possibilities of human errors.
Such processes can be effectively automated by using machine learning to increase organizations testing efficiency and quality of software.
This project aims to provide an AI-based system that automates the process of software testing and detects bugs, and performance issues at very early stages of the development cycle.
Week 1-2: Initial Planning and Requirement Analysis
Define project objectives and scope, and high-level requirements
Gathering of necessary data and resources.
Week 3-4: Data Collection and Preprocessing Phase
Get historical test data and bug reports for training and testing the model.
Extract features and clean data.
Week 5-6: Model Development Phase
Development and training of Machine learning models for bug prediction and Performance Issue Detection.
Validate the accuracy and performance of the trained model using the test data.
Week 7-8: System Implementation Phase
Bug prediction and testing automation implementation.
Integrate developed models into already available test automation frameworks and CI/CD pipelines
Testing and strengthening of the system until it can reach satisfactory performance according to stipulated metrics and feedback from users.
Week 9-10: System Integration and Testing Phase
Testing for accurate bug prediction and test automation thoroughly.
System performance monitoring and analysis of user interactions
Implementing continuous improvement regarding analytics and user feedback.
Week 11-12: Final Evaluation and Reporting Phase
Final evaluation and validation of the AI-based quality assurance system
Compilation of final project report and documentation
Presentations of individual report presentations by students
