Context. The adoption of Machine Learning (ML)-enabled systems is steadily increasing. Nevertheless, there is a shortage of ML-specific quality assurance approaches, possibly because of the limited knowledge of how quality-related concerns emerge and evolve in ML-enabled systems.

Objective. We aim to investigate the emergence and evolution of specific types of quality-related concerns known as ML-specific code smells, i.e., sub-optimal implementation solutions applied on ML pipelines that may significantly decrease both quality and maintainability of ML-enabled systems. More specifically, we present a plan to study ML-specific code smells by empirically analyzing (i) their prevalence in real ML-enabled systems, (ii) how they are introduced and removed, and (iii) their survivability.

Method. We will conduct an exploratory study, mining a large dataset of ML-enabled systems and analyzing over 400k commits about 337 projects. We will track and inspect the introduction and evolution of ML smells through CodeSmile, a novel ML smell detector that we will build to enable our investigation and to detect ML-specific code smells.