IMPLEMENTATION OF SOFTWARE TESTING USING MACHINE LEARNING: A SYSTEMATIC MAPPING STUDY

International Journal of Scientific Innovation & and Development(IJSID) Published by Leilani Katie Publication and PRESS

Abstract

Software testing plays a critical role in ensuring software quality and reliability throughout the software development life cycle. Traditional software testing techniques often require considerable human effort, time, and cost. Recent advances in Machine Learning (ML) have introduced intelligent approaches capable of automating testing activities such as defect prediction, test case generation, fault localization, and regression testing. This systematic mapping study investigates the implementation of software testing using machine learning techniques and provides a comprehensive overview of research trends, methodologies, challenges, and future opportunities. The proposed study presents an ML-based software testing architecture and evaluates experimental outcomes using different datasets and performance metrics.

References

1. Panwar A., Peddi P., “Implementation of Software Testing Using Machine Learning: A Systematic Mapping Study,” 2023. 2. Abdellatif A. et al., “Automated Software Testing Using Machine Learning: A Systematic Mapping Study,” 2024. 3. Riccio V. et al., “Testing Machine Learning Based Systems: A Systematic Mapping,” Empirical Software Engineering. 4. Fontes A., Gay G., “Integration of Machine Learning into Automated Test Generation,” Software Testing, Verification and Reliability. 5. Shafiq S. et al., “Machine Learning for Software Engineering: A Systematic Mapping.” 6. Pressman R., Software Engineering: A Practitioner’s Approach. 7. Han J., Kamber M., Data Mining Concepts and Techniques. 8. Mitchell T., Machine Learning. 9. Sommerville I., Software Engineering. 10. Witten I., Frank E., Practical Machine Learning Tools and Techniques.

Keywords

Data Mining, Healthcare Research, Disease Prediction, Machine Learning, Electronic Health Records, Healthcare Analytics