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 | |
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Corpora | Corpus-Eclipse3.0.zip | Corpus-Rhino.zip |
Features | Eclipse 3.0 features | Rhino features |
Queries | Queries-Eclipse3.0.zip | Queries-Rhino.zip |
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Download all the Eclipse 3.0 data (8.26MB) and Rhino data (486KB).
Results
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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.xlsNotes:
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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.
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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).
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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.