MLSmellHound: A Context-Aware Code Analysis Tool

TL;DR

This paper introduces MLSmellHound, a context-aware code analysis tool that adapts linting results based on project-specific context to improve defect detection and maintainability in ML-integrated software projects.

Contribution

It proposes a novel approach to incorporate contextual information into code analysis, addressing cultural and domain differences in ML software development.

Findings

Enhanced error reporting tailored to project context

Successful adaptation of Pylint with contextual transformations

Improved defect detection relevance in ML projects

Abstract

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.