Requirements Engineering in Machine Learning Projects
Journal
IEEE Access
Date Issued
2023-07-12
Author(s)
Gjorgjevikj, Ana
Antovski, Ljupcho
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 mixedmethod 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.
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 mixedmethod 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.
Subjects
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