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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Vydáno v:	2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS) Ročník 35; s. 65 - 66
Hlavní autoři:	Ziobrowski, Maciej, Ochodek, Miroslaw, Nawrocki, Jerzy, Walter, Bartosz
Médium:	Konferenční příspěvek Journal Article
Jazyk:	angličtina
Vydáno:	Polish Information Processing Society 2023
Témata:	code smell domain specific languages McPython Python Rule mining
ISSN:	2300-5963
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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