Top

Published in:

2024 | OriginalPaper | Chapter

Metrics for Code Smells of ML Pipelines

Authors : Dolors Costal, Cristina Gómez, Silverio Martínez-Fernández

Published in: Product-Focused Software Process Improvement

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

next chapter Declarative Process Mining for Software Processes: The RuM Toolkit and the Declare4Py Python Library

https://www.tensorflow.org

https://keras.io/

https://scikit-learn.org/stable/

https://pytorch.org/

Martínez-Fernández, S., Bogner, J., Franch, X., Oriol, M., et al.: Software engineering for ai-based systems: a survey. TOSEM 31(2), 1–59 (2022)CrossRef

Continuous delivery for machine learning (2019). https://martinfowler.com/articles/cd4ml.html. Accessed 8 Mar 2023

Sculley, D., et al.: Hidden technical debt in machine learning systems. Adv. Neural Inf. Process. Syst. 28, 2503–2511 (2015)

Jebnoun, H., Ben Braiek, H., Rahman, M.M., Khomh, F.: The scent of deep learning code: an empirical study. In: International Conference on Mining Software Repositories 2020, pp. 420–430 (2020)

Tang, Y., Khatchadourian, R., Bagherzadeh, M., Singh, R., Stewart, A., Raja, A.: An empirical study of refactorings and technical debt in machine learning systems. In: International Conference on Software Engineering (ICSE), pp. 238–250. IEEE (2021)

Gesi, J., et al.: Code smells in machine learning systems (2022). arXiv preprint arXiv:2203.00803

Van Oort, B., Cruz, L., Loni, B., Van Deursen, A.: Project smells experiences in analysing the software quality of ML projects with mllint. In: International Conference on Software Engineering: Software Engineering in Practice, pp. 211–220 (2022)

Bafandeh Mayvan, B., Rasoolzadegan, A., Javan Jafari, A.: Bad smell detection using quality metrics and refactoring opportunities. J. Softw. Evol. Process 32(8), 1–33 (2020)CrossRef

Lewis, G.A., Ozkaya, I., Xu, X.: Software architecture challenges for ML systems. In: International Conference on Software Maintenance and Evolution (ICSME), pp. 634–638. IEEE (2021)

10.

Sant’Anna, C., Figueiredo, E., Garcia, A., Lucena, C.J.: On the modularity of software architectures: a concern-driven measurement frame-work. In: Oquendo, F. (eds.) Software Architecture. ECSA 2007. LNCS, vol. 4758, pp. 207–224. Springer, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75132-8_17

11.

Pengo, E., Gál, P.: Grasping primitive enthusiasm-approaching primitive obsession in steps. In: International Conference on Software Technologies, 2018, pp. 423–430 (2018)

12.

Martin, R.C.: Design Principles and Design Patterns. https://web.archive.org/web/20150906155800/http://www.objectmentor.com/resources/articles/Principles_and_Patterns.pdf. Accessed 23 July 2023

13.

Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns. Elements of Reusable Object-Oriented Software, 1st edn. Addison-Wesley, Boston (1995)

14.

Fowler, M.: Refactoring: Improving the Design of Existing Code, 2nd edn. Addison-Wesley, Boston (2019)

15.

Lanza, M., Marinescu, R.: Object-Oriented Metrics in Practice: Using Software Metrics to Characterize Evaluate and Improve the Design of Object-Oriented Systems. Springer, Berlin, Heidelberg (2006). https://doi.org/10.1007/3-540-39538-5

Title: Metrics for Code Smells of ML Pipelines
Authors: Dolors Costal
Cristina Gómez
Silverio Martínez-Fernández
Publisher: Springer Nature Switzerland
Book: Product-Focused Software Process Improvement
Print ISBN: 978-3-031-49268-6

Electronic ISBN: 978-3-031-49269-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-49269-3_1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner