Metrics for code smells of ML pipelines

ML pipelines, as key components of ML systems, shall be developed following quality assurance techniques. Unfortunately, it is often the case in which they present maintainability issues, due to the experimentatal nature of data collection and ML model construction. To address this problem, this work in progress proposes initial metrics to measure the presence of code smells in ML pipelines. These metrics reflect good software engineering practices for code quality of ML pipelines.
This work was supported by the “Spanish Ministerio de Ciencia e Innovación” under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.
Peer Reviewed
Postprint (author's final draft)