Towards Reliable Rule Mining about Code Smells: The McPython Approach

CODE smell is a risky pattern in code that can lead, in the future, to problems with code maintenance. One of the approaches to identifying smells in the code is metric-based smell detection. A classic example is the God Class smell which can be detected by using three metrics (see, e.g., [1]-[3]):...

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Veröffentlicht in:2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS) Jg. 35; S. 65 - 66
Hauptverfasser: Ziobrowski, Maciej, Ochodek, Miroslaw, Nawrocki, Jerzy, Walter, Bartosz
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: Polish Information Processing Society 2023
Schlagworte:
code smell
domain specific languages
McPython
Python
Rule mining
ISSN:2300-5963
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Zusammenfassung:CODE smell is a risky pattern in code that can lead, in the future, to problems with code maintenance. One of the approaches to identifying smells in the code is metric-based smell detection. A classic example is the God Class smell which can be detected by using three metrics (see, e.g., [1]-[3]): * Weighted Method Count (WMC - sum of McCabe's complexity of all methods in the analysed class), * Tight Class Cohesion (TCC - relative number of directly connected methods within the analysed class), and * Access to Foreign Data (ATFD - number of classes containing attributes referenced by the analysed class directly or via get/set methods).
ISSN:2300-5963
DOI:10.15439/2023F2071