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

Bart Van Oort, Luís Cruz, Babak Loni, Arie Van Deursen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
Subtitle of host publicationSoftware Engineering in Practice, ICSE-SEIP 2022
PublisherIEEE
Pages211-220
Number of pages10
ISBN (Electronic)978-1-6654-9590-5
DOIs
Publication statusPublished - 2022
Event44th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022 - Pittsburgh, United States
Duration: 22 May 202227 May 2022

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference44th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022
Country/TerritoryUnited States
CityPittsburgh
Period22/05/2227/05/22

Keywords

  • code smells
  • context-aware
  • dependency management
  • machine learning
  • mllint
  • project smells
  • Python
  • software quality
  • static analysis

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