@INPROCEEDINGS{Yamashota17-MSR-P-Seodin,
   AUTHOR       = {Aiko Yamashita and S. Amirhossein Abtahizadeh and 
      Foutse Khomh and Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 14<sup>th</sup> International Conference on Mining Software Repositories (MSR)},
   TITLE        = {Software Evolution and Quality Data from Controlled, 
      Multiple, Industrial Case Studies},
   YEAR         = {2017},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Lin Tan and Abram Hindle},
   MONTH        = {May},
   NOTE         = {4 pages. Short paper.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {507--510},
   PUBLISHER    = {ACM Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Program comprehension</b>, Venue: <c>MSR</c>},
   URL          = {http://www.ptidej.net/publications/documents/MSR17a.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/MSR17a.ppt.pdf},
   ABSTRACT     = {A main difficulty to study the evolution and quality of 
      real-life software systems is the effect of moderator factors, such 
      as: programming skill, type of maintenance task, and learning effect. 
      Experimenters must account for moderator factors to identify the 
      relationships between the variables of interest. In practice, 
      controlling for moderator factors in realistic (industrial) settings 
      is expensive and rather difficult. The data presented in this paper 
      has two particularities: First, it involves six professional 
      developers and four real-life, industrial systems. Second, it was 
      obtained from controlled, multiple case studies where the moderator 
      variables: programming skill, maintenance task, and learning effect 
      were controlled for. This data set is relevant to experimenters 
      studying evolution and quality of real-life systems, in particular 
      those interested in studying industrial systems and replicating 
      empirical studies.}
}

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