"Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint

TL;DR

This paper introduces the concept of project smells to assess software quality in ML projects, presents the mllint static analysis tool, and evaluates their effectiveness and relevance in an industrial setting.

Contribution

It proposes the novel concept of project smells for holistic ML project quality assessment and develops the open-source mllint tool for static analysis of these issues.

Findings

Project smells are perceived as important for ML project quality.

Context-aware static analysis tools are preferred by practitioners.

Minimal configuration is essential for practical adoption of mllint.

Abstract

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user.