Towards reliable rule mining about code smells: The McPython approach (Invited Lecture — Extended Abstract)

• Autorzy: Ziobrowski Maciej , Ochodek Mirosław , Nawrocki Jerzy , Walter Bartosz

Identyfikatory

DOI

10.15439/2023F2071

• Języki publikacji: EN

Abstrakty

EN

Code smell is a risky code pattern impacting code maintenance. Some of the code smells are defined by metrics (e.g., lines of code). Unfortunately, it is not clear how to set these thresholds for them. Goal: To propose a smell description language that allows querying code repositories to empirically determine impact of metric thresholds on severity of smells. Method: We propose a language, called McPython, that allows defining metric-based smells. We evaluate the expressiveness of the language by specifying some popular code smells. Results: McPython is a functional domain-specific language that allows defining smells as parameterized logical propositions with auxiliary functions. McPython code is translated to Python and executed on object-oriented representation of a code repository. Its current version is capable of expressing 7 code smells. Conclusion: Despite its limitations, McPython has the potential to help in investigating the impact of code smell parameters on their severity.

Słowa kluczowe

EN

rules mining; code smell; McPython; Python; domain specific languages

PL

eksploracja reguł; zapachy kodu; McPython; Python; język specyficzny dla domeny

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 65--66
• Opis fizyczny: Bibliogr. 10 poz., il.

Twórcy

autor

Ziobrowski Maciej

• Poznan University of Technology, Poznań, Poland

autor

Ochodek Mirosław

• Poznan University of Technology, Poznań, Poland

autor

Nawrocki Jerzy

• Poznan University of Technology, Poznań, Poland

autor

Walter Bartosz

• Poznan University of Technology, Poznań, Poland

Bibliografia

• [1] S. M. Olbrich, D. S. Cruzes, and D. I. Sjøberg, “Are all code smells harmful? a study of god classes and brain classes in the evolution of three open source systems,” in 2010 IEEE International Conference on Software Maintenance, 2010, pp. 1–10.
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• [3] Łukasz Puławski, “Methods of detecting spatio-temporal patterns in software development processes,” Ph.D. dissertation, University of Warsaw, 2022. [Online]. Available: https://ornak.icm.edu.pl/bitstream/handle/item/4533/0000-DR-1827-praca.pdf?sequence=1
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• [7] J. R. Quinlan, C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1993.
• [8] “Weka 3: Machine learning software in java,” https://www.cs.waikato.ac.nz/ml/weka/.
• [9] Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko, “Rough sets,” Commun. ACM, vol. 38, no. 11, p. 88–95, nov 1995. [Online]. Available: https://doi.org/10.1145/219717.219791
• [10] D. Strein, R. Lincke, J. Lundberg, and W. L¨owe, “An extensible metamodel for program analysis,” IEEE Transactions on Software Engineering, vol. 33, no. 9, pp. 592–607, 2007.

Uwagi

1. Main Track Invited Contributions

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).