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Introduction to Smells

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Reference

Studies who have been using the data (in any form) are required to include the following reference:

@inproceedings{DBLP:conf/icse/TufanoPBOPLP15,
  author    = {Michele Tufano and
               Fabio Palomba and
               Gabriele Bavota and
               Rocco Oliveto and
               Massimiliano Di Penta and
               Andrea De Lucia and
               Denys Poshyvanyk},
  title     = {When and Why Your Code Starts to Smell Bad},
  booktitle = {37th {IEEE/ACM} International Conference on Software Engineering,
               {ICSE} 2015, Florence, Italy, May 16-24, 2015, Volume 1},
  pages     = {403--414},
  year      = {2015},
  crossref  = {DBLP:conf/icse/2015-1},
  url       = {http://dx.doi.org/10.1109/ICSE.2015.59},
  doi       = {10.1109/ICSE.2015.59},
  timestamp = {Thu, 10 Sep 2015 14:02:15 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/icse/TufanoPBOPLP15},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

About the Data

Overview of Data

While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.

Paper Abstract

In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.