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]):...

Celý popis

Uloženo v:
Podrobná bibliografie
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!
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).
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
BookMark eNo9zE9PwkAQBfDVaCIiF88e-gWquzuzu603QkBJMBrEczP7p1BSuqSFGL69jRgv8zLvJb9bdtXEJjB2L_ijUAj5k-QSZpIbccFGuckyyLVBNBlesoEEzlOVa7hho67bcs6lQC5RD9h0Fb-p9V2yDHVFtg7J8tift6qpmnVCNh4PyST6kHzuQl13z8lq06_u43TYxCYZ7_dtJLe5Y9cl1V0Y_eWQfc2mq8lrunh_mU_Gi9RLKUSKPLMZCU6AQnOXkxdZCRZdIBWUyz0BBfBaEVqtLeSojHbWGQdgy2BgyOZn10faFvu22lF7KiJVxW8R23VB7aFydSi85bxEDEQSEZUm59GoEmTpXAiBeuvhbFX9-28JDlyLDOAHy0dlMQ
ContentType Conference Proceeding
Journal Article
DBID 6IE
6IL
CBEJK
RIE
RIL
DOA
DOI 10.15439/2023F2071
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DOAJ Directory of Open Access Journals
DatabaseTitleList
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISBN 9788396744784
8396744785
EISSN 2300-5963
EndPage 66
ExternalDocumentID oai_doaj_org_article_db00f44eaa244456acd475f32fcceeea
10306183
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
6IF
6IN
AAJGR
AAWTH
ABLEC
ADBBV
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BCNDV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CHZPO
GROUPED_DOAJ
IEGSK
M~E
OCL
OK1
Y2W
ID FETCH-LOGICAL-d2211-408b8a10a34160c9ad18f3b4cea5e5c9da3ae3d65a4b66b394576cbc7c33bfe73
IEDL.DBID DOA
IngestDate Fri Oct 03 12:51:46 EDT 2025
Wed Aug 27 02:32:14 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-d2211-408b8a10a34160c9ad18f3b4cea5e5c9da3ae3d65a4b66b394576cbc7c33bfe73
OpenAccessLink https://doaj.org/article/db00f44eaa244456acd475f32fcceeea
PageCount 2
ParticipantIDs doaj_primary_oai_doaj_org_article_db00f44eaa244456acd475f32fcceeea
ieee_primary_10306183
PublicationCentury 2000
PublicationDate 2023-00-00
PublicationDateYYYYMMDD 2023-01-01
PublicationDate_xml – year: 2023
  text: 2023-00-00
PublicationDecade 2020
PublicationTitle 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS)
PublicationTitleAbbrev FEDCSIS
PublicationYear 2023
Publisher Polish Information Processing Society
Publisher_xml – name: Polish Information Processing Society
SSID ssj0002140246
Score 2.212722
Snippet 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...
SourceID doaj
ieee
SourceType Open Website
Publisher
StartPage 65
SubjectTerms code smell
domain specific languages
McPython
Python
Rule mining
SummonAdditionalLinks – databaseName: IEEE Electronic Library (IEL)
  dbid: RIE
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NS8NAEF1s8eBJxYr1iz14jSbZZDfrTUuLl5aiFXoLs5MJCLWVfgj-e2e3UfHgwUsIe0jgzWRmZ_PejBBXloNinZOOyBngAsXayIHGyKkshYSdJMEiDJswo1ExndpxI1YPWhgiCuQzuva34V9-tcCNPyq78SOxNPtgS7SM0VuxVtNyNOfE6gt5NUjjIIf35LJfE1NCwhjs__NVB6LzI72T4--kcih2aH4k-pNAbl1JTyD2Wif5uOHLMAx3kIFbLHuLiuTTK81mq1vJxpdDHH_4xgDyrmkb3hHPg_6k9xA18w-iKuW6jJErXAFJDJxpdIwWqqSolcuQIKccbQUKSFU6h8xp7ZTNuHhAhwaVcjUZdSza88WcToSMMbUxgUoRLYdGC4YjJILPVaDrJO6Kew9Y-bZtcVH6ptNhgUEpGx8uK_5E6ywjAN4T8MYLsMpMXqu0RkaFoCs6Hsjvh3xhePrH-pnY8xbbHmici_Z6uaELsYvv65fV8jJY9xNse6eo
  priority: 102
  providerName: IEEE
Title Towards Reliable Rule Mining about Code Smells: The McPython Approach
URI https://ieeexplore.ieee.org/document/10306183
https://doaj.org/article/db00f44eaa244456acd475f32fcceeea
Volume 35
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEF6kePDis2J9lD140EMwySabrLcqLQq29FChtzI72UChrZI-wIv4I_yF_hJnN6nUkxcvewhhAzOz88rs9zF2qcgp5rGRntEJUIGilKdBoqdFFEJARhJg6sgmkl4vHQ5Vf4Pqy86ElfDApeBuMrKLPIoMAAUiivaAWZTEuQhzJP9uXGrkJ2qjmLI-OKS6IYxkhUcaU9S1Vb7o0KtBhc7_i07FRZPOPtut0kDeKj9_wLbM7JDtrSkWeHXijth04MZa57wwk7G95cSLJS1TR-vA3VQxt7fS-XxqJpP5LSe18y723ywkAF8DhvOrx9nK5pb8qfxlwL8-Pnm76n_zlrb9Dlxc19lzpz24f_AqhgQvC6lyI9mmOoXAB4pF0kcFWZDmQkdoIDYxqgwEGJHJGCItpRYqovICNSYohM5NIo5ZbfYyMyeM-xgq34AIERU5TwUJ-VAEG81A5oHfYHdWaqPXEgRjZGGp3QNS1qhS1ugvZTVY3cr8ZxPLcSbJqZz-x-ZnbMfquOyPnLPaoliaC7aNq8V4XjSdfdDafW833S2_b5P6xUg
linkProvider Directory of Open Access Journals
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEF58gZ5UrFife_AaTbKbx3pTaanYlqIVeguzkwkItZXWCv57Z7dR8eDBSwh7SOCbyczO5vtmhDg3HBSrhNKAbAZcoBgTWEgxsErHELGTRJj7YRNZv5-PRmZQi9W9FoaIPPmMLtyt_5dfTnHhjsou3UislH1wVawnWsfhUq5VNx1NOLW6Ul6149AL4h297NfMFJ8y2tv_fNmOaPyI7-TgO63sihWa7InW0NNb59JRiJ3aST4s-NLz4x2kZxfL22lJ8vGFxuP5lWTzyx4OPlxrAHldNw5viKd2a3jbCeoJCEEZc2XG2OU2hygEzjVpiAbKKK-U1UiQUIKmBAWkyjQBbdPUKqO5fECLGSplK8rUvlibTCd0IGSIsQkJVIxoODgayDhGIrhsBWkVhU1x4wArXpdNLgrXdtovMChF7cVFyR9ppTUB8K6At16Apc6SSsUVMioETdFwQH4_5AvDwz_Wz8RmZ9jrFt27_v2R2HLWWx5vHIu1t9mCTsQGvr89z2en3tKfUOKq7w
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+18th+Conference+on+Computer+Science+and+Intelligence+Systems+%28FedCSIS%29&rft.atitle=Towards+Reliable+Rule+Mining+about+Code+Smells%3A+The+McPython+Approach&rft.au=Ziobrowski%2C+Maciej&rft.au=Ochodek%2C+Miroslaw&rft.au=Nawrocki%2C+Jerzy&rft.au=Walter%2C+Bartosz&rft.date=2023-01-01&rft.pub=Polish+Information+Processing+Society&rft.spage=65&rft.epage=66&rft_id=info:doi/10.15439%2F2023F2071&rft.externalDocID=10306183