When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910365676208128 |
|---|---|
| author | Recupito, Gilberto Giordano, Giammaria Ferrucci, Filomena Di Nucci, Dario Palomba, Fabio |
| author_facet | Recupito, Gilberto Giordano, Giammaria Ferrucci, Filomena Di Nucci, Dario Palomba, Fabio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems Recupito, Gilberto Giordano, Giammaria Ferrucci, Filomena Di Nucci, Dario Palomba, Fabio Software Engineering D.2.7 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. |
| title | When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems |
| topic | Software Engineering D.2.7 |
| url | https://arxiv.org/abs/2403.08311 |