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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| Vydané v: | 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS) Ročník 35; s. 65 - 66 |
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| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. Journal Article |
| Jazyk: | English |
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Polish Information Processing Society
2023
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| ISSN: | 2300-5963 |
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| Abstract | 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). |
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| AbstractList | 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). |
| Author | Walter, Bartosz Ziobrowski, Maciej Ochodek, Miroslaw Nawrocki, Jerzy |
| Author_xml | – sequence: 1 givenname: Maciej surname: Ziobrowski fullname: Ziobrowski, Maciej email: maciej.ziobrowski@student.put.poznan.pl organization: Poznan University of Technology,Poznań,Poland – sequence: 2 givenname: Miroslaw surname: Ochodek fullname: Ochodek, Miroslaw email: miroslaw.ochodek@put.poznan.pl organization: Poznan University of Technology,Poznań,Poland – sequence: 3 givenname: Jerzy surname: Nawrocki fullname: Nawrocki, Jerzy email: jerzy.nawrocki@put.poznan.pl organization: Poznan University of Technology,Poznań,Poland – sequence: 4 givenname: Bartosz surname: Walter fullname: Walter, Bartosz email: bartosz.walter@put.poznan.pl organization: Poznan University of Technology,Poznań,Poland |
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| SubjectTerms | code smell domain specific languages McPython Python Rule mining |
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| Title | Towards Reliable Rule Mining about Code Smells: The McPython Approach |
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