Using Data Fusion and Web Mining to Support Feature Location in Software - Online Appendix

This web page is a companion to our International Conference on Program Comprehension publication entitled "Using Data Fusion and Web Mining to Support Feature Location in Software"

Data

Eclipse 3.0 Rhino
Corpora Corpus-Eclipse3.0.zip Corpus-Rhino.zip
Features Eclipse 3.0 features Rhino features
Queries Queries-Eclipse3.0.zip Queries-Rhino.zip

Results

  • Download the data used to compute the effectiveness measure for the feature location techniques that combine information retrieval, dynamic information and web mining (IR+Dyn+WebMining), when we filter top x methods and bottom x methods (x=0%, 10%, …, 100%).
    EclipseIRDynAllTopAndBottom.xls
    RhinoIRDynAllTopAndBottom.xls

    Notes:

    • The numbers in the red worksheets represent the ranks of the methods in the gold set, after filtering X% of methods (X is denoted by the column header). Each red tab (that contains the raw data) has a corresponding tab which visualizes the data using box plots.

    • Where the word "Binary" is not explicitly stated in the worksheets names, we refer to using the frequency weights (as opposed to the binary weights).

  • The results of comparing the all the feature location techniques based on their effectiveness can be downloaded here:
    EffectivenessEclipse3.0.xls
    EffectivenessRhino.xls

Participants

  • Meghan Revelle

    E-mail: meghan at cs dot wm dot edu

  • Bogdan Dit

    E-mail: bdit at cs dot wm dot edu

  • Denys Poshyvanyk

    E-mail: denys at cs dot wm dot edu


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