Requirements Engineering in Machine Learning Projects

Abstract

Over the last decade, machine learning methods have revolutionized a large number of domains and provided solutions to many problems that people could hardly solve in the past. The availability of large amounts of data, powerful processing architectures, and easy-to-use software frameworks have made machine learning a popular, readily available, and affordable option in many different domains and contexts. However, the development and maintenance of production-level machine learning systems have proven to be quite challenging, as these activities require an engineering approach and solid best practices. Software engineering offers a mature development process and best practices for conventional software systems, but some of them are not directly applicable to the new programming paradigm imposed by machine learning. The same applies to the requirements engineering best practices. Therefore, this article provides an overview of the requirements engineering challenges in the development of machine learning systems that have been reported in the research literature, along with their proposed solutions. Furthermore, it presents our approach to overcoming those challenges in the form of a case study. Through this mixed-method study, the article tries to identify the necessary adjustments to (1) the best practices for conventional requirements engineering and (2) the conventional understanding of certain types of requirements to better fit the specifics of machine learning. Moreover, the article tries to emphasize the relevance of properly conducted requirements engineering activities in addressing the complexity of machine learning systems, as well as to motivate further discussion on the requirements engineering best practices in developing such systems.