When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems
<|reference_start|>When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems: 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 the 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.<|reference_end|>
@article{recupito2024when,
    title={When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in
  ML-enabled Systems},
    author={Gilberto Recupito and Giammaria Giordano and Filomena Ferrucci and
  Dario Di Nucci and Fabio Palomba},
    journal={arXiv preprint arXiv:2403.08311},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08311},
    primaryClass={cs.SE}
}