Filtering Code Smells Detection Results

被引:15
|
作者
Fontana, Francesca Arcelli [1 ]
Ferme, Vincenzo [2 ]
Zanoni, Marco [1 ]
机构
[1] Univ Milano Bicocca, Dept Informat Syst & Commun, Milan, Italy
[2] Univ Lugano USI, Fac Informat, Lugano, Switzerland
来源
2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol 2 | 2015年
关键词
D O I
10.1109/ICSE.2015.256
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Many tools for code smell detection have been developed, providing often different results. This is due to the informal definition of code smells and to the subjective interpretation of them. Usually, aspects related to the domain, size, and design of the system are not taken into account when detecting and analyzing smells. These aspects can be used to filter out the noise and achieve more relevant results. In this paper, we propose different filters that we have identified for five code smells. We provide two kind of filters, Strong and Weak Filters, that can be integrated as part of a detection approach.
引用
收藏
页码:803 / 804
页数:2
相关论文
共 50 条
  • [41] CODE-SMELLS IN AOP
    Draganescu, Serban
    Tapus, Nicolae
    UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2010, 72 (04): : 3 - 12
  • [42] An Analytical Study of Code Smells
    Bamizadeh, Lida
    Kumar, Binod
    Kumar, Ajay
    Shirwaikar, Shailaja
    TEHNICKI GLASNIK-TECHNICAL JOURNAL, 2021, 15 (01): : 121 - 126
  • [43] Different Kind of Smells: Security Smells in Infrastructure as Code Scripts
    Rahman, Akond
    Williams, Laurie
    IEEE SECURITY & PRIVACY, 2021, 19 (03) : 33 - 41
  • [44] Visualizing Code Bad Smells
    Hammad, Maen
    Alsofriya, Sabah
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2019, 10 (05) : 281 - 286
  • [45] Are architectural smells independent from code smells? An empirical study
    Fontana, Francesca Arcelli
    Lenarduzzi, Valentina
    Roveda, Riccardo
    Taibi, Davide
    JOURNAL OF SYSTEMS AND SOFTWARE, 2019, 154 : 139 - 156
  • [46] Automatic detection of Long Method and God Class code smells through neural source code embeddings
    Kovacevic, Aleksandar
    Slivka, Jelena
    Vidakovic, Dragan
    Grujic, Katarina-Glorija
    Luburic, Nikola
    Prokic, Simona
    Sladic, Goran
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 204
  • [47] Python']Python code smells detection using conventional machine learning models
    Sandouka, Rana
    Aljamaan, Hamoud
    PEERJ COMPUTER SCIENCE, 2023, 9
  • [48] A Novel Tree-based Neural Network for Android Code Smells Detection
    Yu, Jing
    Mao, Chenguang
    Ye, Xiaojun
    2021 IEEE 21ST INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS 2021), 2021, : 738 - 748
  • [49] Detection of Unused Native Methods code smells in Multi-Language Systems
    Ansari, Md. Shahrukh
    Moiz, Salman Abdul
    2024 4TH INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SOFTWARE ENGINEERING, ICICSE 2024, 2024, : 44 - 50
  • [50] Do code reviews lead to fewer code smells?
    Tuna, Erdem
    Seaman, Carolyn
    Tuzun, Eray
    JOURNAL OF SYSTEMS AND SOFTWARE, 2024, 215