ERA Track


The Early Research Achievements (ERA) Track aims at providing researchers with a forum for discussing novel research ideas in early stages of development. The topics of interest for this track are the same as the main research track, but we aim at creating a stimulating atmosphere where researchers can present and get early feedback on promising work that has not yet been fully evaluated.


Session 1 – Maintenance and Co.

Thursday, February 6, 2014, from 10:30 to 12:30 
Session Chairs: Andy Zaidman, Coen De Roover

  • Scott Grant and James R. Cordy.
    Examining the Relationship Between  Topic Model Similarity and Software Maintenance  
  • Maelick Claes, Tom Mens,  and Philippe Grosjean. 
    On the  maintainability of CRAN packages  
  • Vadim Zaytsev. 
    Formal Foundations for Semi-parsing
  • Emily Hill, Bunyamin Sisman and Avinash Kak.
    On the Use of  Positional Proximity in IR-based Feature Location
  • Yuki Kashiwabara, Yuya Onizuka, Takashi Ishio, Yasuhiro Hayase,  Tetsuo Yamamoto and Katsuro Inoue.
    Recommending Verbs for Rename Method Using Association Rule Mining    
  • Toshihiro Kamiya. An Algorithm for Keyword Search on an Execution  Path
  • Ralf Lmmel, Martin Leinberger, Thomas Schmorleiz  and Andrei Varanovich. 
    Comparison of feature implementations  across languages and technologies and styles
  • Federico Tomassetti, Giuseppe Rizzo and Marco Torchiano.
    Spotting  Automatically Cross-Language Relations

Session 2 – Change and Co-Evolution

Thursday, February 6, 2014, from 15:30 to 17:00 
Session Chairs: Andy Zaidman, Coen De Roover

  • Haidar Osman, Mircea Lungu and Oscar Nierstrasz.
    Mining Frequent  Bug-Fix Code Changes
  • Shane Mcintosh, Katie Legere and Ahmed E. Hassan.
    Orchestrating Change: An Artistic Representation of Software Evolution
  • Mathieu Goeminne, Tom Mens and Alexandre Decan.
    Co-evolving  code-related and database-related activities in a data-intensive  software system
  • Manishankar Mondal, Chanchal K. Roy and Kevin Schneider.
    Improving  the Detection Accuracy of Evolutionary Coupling by Measuring Change Correspondence