Summary
Data for machine learning systems is crucial – without data, there can be no machine learning systems. In most machine learning literature, the process of training models usually starts with the data in tabular form. In software engineering, however, this is an intermediate step. The data is collected from source systems and needs to be processed.
In this chapter, we learned how to access data from modern software engineering systems such as Gerrit, GitHub, JIRA, and Git. The code included in this chapter illustrates how to collect data that can be used for further steps in the machine learning pipeline – feature extraction. We’ll focus on this in the next chapter.
Collecting data is not the only preprocessing step that is required to design and develop a reliable software system. Quantifying and monitoring information (and data) quality is equally important. We need to check that the data is fresh (timely) and that there are no problems in preprocessing...
