2021/02/19 by Raju Raju, Raju
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Computers and Society (cs.CY) #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2102.10961
openalex publication_date 2021/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry, Automobile, and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering, but the same cannot be applied in as-is form to the ML testing and we will tell you why.