Can Better Identifier Splitting Techniques Help Feature Location? - Online Appendix

This web page is a companion to our 19th IEEE International Conference on Program Comprehension (ICPC 2011) submission entitled "Can Better Identifier Splitting Techniques Help Feature Location?"

Dit, B., Guerrouj, L., Poshyvanyk, D., and Antoniol, G., "Can Better Identifier Splitting Techniques Help Feature Location?", in Proc. of 19th IEEE International Conference on Program Comprehension (ICPC'11), Kingston, Ontario, Canada, June 22 - June 24 2011, pp. 11-20 (24% acceptance rate) [pdf][slides]

Results

The spreadsheet EffectivenessRhinojEdit.xls contains the effectiveness measure of the two feature location techniques (i.e., IR and IRDyn) using the three splitting algorithms: CamelCase, Samurai and Oracle. The spreadsheet also contains information about the effectiveness measure for the four datasets (i.e., RhinoFeatures, RhinoBugs, jEditFeatures and jEditBugs). The spreadsheet's worksheets are color coded as follows:

  • The yellow worksheets display the box plots (see Figure 1 and Figure 2)

  • The red worksheets show the effectiveness measures of the of FLT from the column for the feature/bug from the right

  • The blue worksheets contain the data for the percentages of times the effectiveness of the FLT from the row is higher than the effectiveness of the FLT from the column (see Table 4 and Table 5)

  • The green worksheets contain the p-values of the Wilcoxon signed-rank test (see Table 6 and Table 7)

Participants

  • Bogdan Dit, The College of William and Mary

    E-mail: bdit at cs dot wm dot edu

  • Latifa Guerrouj, École Polytechnique de Montréal

    E-mail: latifa dot guerrouj at polymtl dot ca

  • Denys Poshyvanyk, The College of William and Mary

    E-mail: denys at cs dot wm dot edu

  • Giuliano Antoniol, École Polytechnique de Montréal

    E-mail: giuliano dot antoniol at polymtl dot ca


We gratefully acknowledge financial support from the NSF on this research project.