Software Framework for Data Fault Injection to Test Machine Learning Systems
Jukka K. Nurminen, Tuomas Halvari, Juha Harviainen, Juha Mylläri, Antti Röyskö, Juuso Silvennoinen, Tommi Mikkonen · 2019
Data-intensive systems are sensitive to the quality of data. Data often has problems due to faulty sensors or network problems, for instance. In this work, we develop a software framework to emulate faults in data and use it to study how machine learning (ML) systems work when the data has problems. We aim for flexibility: users can use predefined or their own dedicated fault models. Likewise, different kind of data (e.g. text, time series, video) can be used and the system under test can vary from a single ML model to a complicated software system. Our goal is to show how data faults can be emulated and how that can be used in the study and development of ML solutions.