Open Access
Automated Software Testing with Machine Learning Techniques
Victoria Schmidt
1
,
Oluwaseun Adeyemi
2
,
Zehra Türkoğlu
3
,
Lucas Fernandez
4
,
Ingrid Johansson
5
,
Oluwaseun Adeyemi
2
,
Zehra Türkoğlu
3
,
Lucas Fernandez
4
,
Ingrid Johansson
5
1
ETH Zurich, Switzerland
2
University of Lagos, Nigeria
3
Sabanci University, Turkey
4
University of Buenos Aires, Argentina
5
Uppsala University, Sweden
Abstract
This research explores advanced techniques for this research area. We introduce a hybrid model combining advanced algorithms with modern techniques to improve accuracy. Our experiments on diverse datasets demonstrate superior performance compared to existing baseline methods. The proposed framework achieves 44% accuracy on standard benchmark datasets.
Keywords
cybersecurity,network security,intrusion detection,privacy,encryption
How to Cite
Schmidt, V., Adeyemi, O., Türkoğlu, Z., Fernandez, L., & Johansson, I. (2019). Automated Software Testing with Machine Learning Techniques. Nivo Light - International Journal of Research & Innovation, 1(3), 39–46. https://doi.org/10.28051/ojstest-17
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