Practice-based Collection of Bad Smells in Machine Learning Projects

Abstract

In this study, we consider projects for developing service systems using machine learning (ML) techniques. As ML techniques have been introduced in various domains, there is reusable knowledge on ML projects that can be employed for conducting such projects without facing major failures. The usage of such knowledge during a project has not yet been clearly described in the form of reusable knowledge such as best practices or patterns. Thus, in this study, we propose a method for collecting the ominous signs in ML projects as “bad smells” and incorporating them as a part of such reusable knowledge. We confirmed the effectiveness of the proposed method through an evaluation.