@INPROCEEDINGS{Washizaki19-IWESEP-DesignPatternsforMLSystems,
   AUTHOR       = {Hironori Washizaki and Hiromu Uchida and Foutse Khomh and 
      Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 10<sup>th</sup> International Workshop on Empirical Software Engineering in Practice (IWESEP)},
   TITLE        = {Studying Software Engineering Patterns for Designing 
      Machine Learning Systems},
   YEAR         = {2019},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Akinori Ihara and Marco Aur�lio Gerosa},
   MONTH        = {December},
   NOTE         = {6 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {49--54},
   PUBLISHER    = {IEEE CS Society},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Code and design smells</b>, 
      Venue: <i>IWESEP</i>},
   URL          = {http://www.ptidej.net/publications/documents/IWESEP19.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/IWESEP19.ppt.pdf},
   ABSTRACT     = {Machine-learning (ML) techniques are becoming more 
      prevalent. ML techniques rely on mathematics and software 
      engineering. Researchers and practitioners studying best practices 
      strive to design ML systems and software that address software 
      complexity and quality issues. Such design practices are often 
      formalized as architecture and design patterns by encapsulating 
      reusable solutions to common problems within given contexts. However, 
      a systematic study to collect, classify, and discuss these 
      software-engineering (SE) design patterns for ML techniques have yet 
      to be reported. Our research collects good/bad SE design patterns for 
      ML techniques to provide developers with a comprehensive 
      classification of such patterns. Herein we report the preliminary 
      results of a systematic-literature review (SLR) of good/bad design 
      patterns for ML.}
}

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ML techniques rely on mathematics and software engineering. Researchers and practitioners studying best practices strive to design ML systems and software that address software complexity and quality issues. Such design practices are often formalized as architecture and design patterns by encapsulating reusable solutions to common problems within given contexts. However, a systematic study to collect, classify, and discuss these software-engineering (SE) design patterns for ML techniques have yet to be reported. Our research collects good/bad SE design patterns for ML techniques to provide developers with a comprehensive classification of such patterns. 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ML techniques rely on mathematics and software \r\n engineering. Researchers and practitioners studying best practices \r\n strive to design ML systems and software that address software \r\n complexity and quality issues. Such design practices are often \r\n formalized as architecture and design patterns by encapsulating \r\n reusable solutions to common problems within given contexts. However, \r\n a systematic study to collect, classify, and discuss these \r\n software-engineering (SE) design patterns for ML techniques have yet \r\n to be reported. Our research collects good/bad SE design patterns for \r\n ML techniques to provide developers with a comprehensive \r\n classification of such patterns. Herein we report the preliminary \r\n results of a systematic-literature review (SLR) of good/bad design \r\n patterns for ML.}\r\n}\r\n\r\n","author_short":["Washizaki, H.","Uchida, H.","Khomh, F.","Gu�h�neuc, Y."],"editor_short":["Ihara, A.","Gerosa, M. 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