MLSmellHound: A Context-Aware Code Analysis Tool

Jai Kannan, Scott Barnett, Luís Cruz, Anj Simmons, Akash Agarwal

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

1 Citation (Scopus)
4 Downloads (Pure)


Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure maintainability, developers use code analysis tools to aid them in identifying defects and maintaining standards. With the inclusion of machine learning, tools must account for the cultural differences within the teams which manifests as multiple programming languages, and conflicting definitions and objectives. Existing tools fail to identify these cultural differences and are geared towards software engineering which reduces their adoption in ML projects. In our approach we attempt to resolve this problem by exploring the use of context which includes i) purpose of the source code, ii) technical domain, iii) problem domain, iv) team norms, v) operational environment, and vi) development lifecycle stage to provide contextualised error reporting for code analysis. To demonstrate our approach, we adapt Pylint as an example and apply a set of contextual transformations to the linting results based on the domain of individual project files under analysis. This allows for contextualised and meaningful error reporting for the end user.

Original languageEnglish
Title of host publicationProceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
Subtitle of host publicationNew Ideas and Emerging Results, ICSE-NIER 2022
Number of pages5
ISBN (Electronic)978-1-6654-9596-7
Publication statusPublished - 2022
Event44th ACM/IEEE International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2022 - Pittsburgh, United States
Duration: 22 May 202227 May 2022

Publication series

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


Conference44th ACM/IEEE International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2022
Country/TerritoryUnited States

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • code smells
  • context-aware
  • Machine learning


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