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"


Eclipse 3.0 Rhino
Features Eclipse 3.0 features Rhino features


  • 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%).


    • 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:


  • 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.