This repository is a companion page for our ICGSE 2020 paper "On the Detection of Community Smells using Genetic Programming-based Ensemble Classifier Chain".

It contains all the material required to replicate our analysis, including (i) the raw input data (ii) the list of studied projects, and (iii) the features for each smell instance. Some additional data are not included in the paper due to space limitations, are also provided.

Please, use the following bibtex entry:

@inproceedings{almarimi2020community,
  title={On the Detection of Community Smells using Genetic Programming-based Ensemble Classifier Chain},
  author={Almarimi, Nuri and Ouni, Ali and Chouchen, Moataz and Saidani, Islem and Mkaouer, Mohamed Wiem},
  booktitle={15th IEEE/ACM International Conference on Global Software Engineering (ICGSE)},
  pages={1--12},
  year={2020}
}

The data is presented in CSV format and can be directly imported in R and Weka.

Dataset-community-smells-Features.csv: The list of projects with their metrics values.

Dataset-community-smells-list.csv: The list of projects with their identified smells. .

NoD : Number of developers

TAP : Number of active days of an author on a Project

LCP : Number of changed lines of a code per author in a project

CD : Number of core developers

RCD : Ratio of core developers

SD : Number of sponsored developers

RSD : Ratio of sponsored developers

DC : Graph Degree centrality

BC : Graph Betweenness centrality

CC : Graph Closeness centrality

ND : Network Density

NC : Number of communities

RCC : Ratio of commits per community

RDC : Ratio of developers per community

TZ : Number of time zones

RCZ : Ratio of commits per time zone

RDZ : Ratio of developers per time zone

NR : Number of Releases in a project

RCR : Ratio of Commits per Release

FN : Formal network

BFN : Bus Factor Number

TFN : Truck Factor Number

TFC : Truck Factor Coverage