Version Tracking Visualization

Results 1/21/14

https://www.dropbox.com/sh/0ubpqtah1g4u504/3Rwz1P1cy-/Graphs

Results of A/B/C/D prediction: dismal

http://unbox.org/things/var/ben/consolidated/logs/log_20140109125621_1.txt

Results 2:

Back to the CSV: class names are listed

['ant-1.3.csv', 'ant-1.4.csv', 'ant-1.5.csv', 'ant-1.6.csv', 'ant-1.7.csv']
Type A: 4%    B: 11%    C: 12%    D: 71%    NoMatch: 0%
Type A: 3%    B: 17%    C: 8%    D: 63%    NoMatch: 5%
Type A: 5%    B: 5%    C: 18%    D: 69%    NoMatch: 0%
Type A: 17%    B: 8%    C: 15%    D: 58%    NoMatch: 0%

['camel-1.0.csv', 'camel-1.2.csv', 'camel-1.4.csv', 'camel-1.6.csv']
Type A: 3%    B: 0%    C: 22%    D: 51%    NoMatch: 22%
Type A: 15%    B: 18%    C: 3%    D: 55%    NoMatch: 6%
Type A: 9%    B: 7%    C: 10%    D: 71%    NoMatch: 1%

['ivy-1.1.csv', 'ivy-1.4.csv', 'ivy-2.0.csv']
Type A: 7%    B: 47%    C: 2%    D: 40%    NoMatch: 1%
Type A: 0%    B: 0%    C: 0%    D: 0%    NoMatch: 100%

['jedit-3.2.csv', 'jedit-4.0.csv', 'jedit-4.1.csv', 'jedit-4.2.csv', 'jedit-4.3.csv']
Type A: 17%    B: 15%    C: 5%    D: 58%    NoMatch: 2%
Type A: 16%    B: 7%    C: 9%    D: 62%    NoMatch: 4%
Type A: 9%    B: 15%    C: 3%    D: 64%    NoMatch: 6%
Type A: 0%    B: 11%    C: 0%    D: 47%    NoMatch: 38%

['log4j-1.0.csv', 'log4j-1.1.csv', 'log4j-1.2.csv']
Type A: 16%    B: 6%    C: 8%    D: 41%    NoMatch: 27%
Type A: 30%    B: 1%    C: 56%    D: 5%    NoMatch: 5%

['lucene-2.0.csv', 'lucene-2.2.csv', 'lucene-2.4.csv']
Type A: 33%    B: 12%    C: 24%    D: 28%    NoMatch: 1%
Type A: 42%    B: 15%    C: 21%    D: 15%    NoMatch: 4%

['synapse-1.0.csv', 'synapse-1.1.csv', 'synapse-1.2.csv']
Type A: 5%    B: 4%    C: 22%    D: 63%    NoMatch: 3%
Type A: 13%    B: 12%    C: 19%    D: 53%    NoMatch: 1%

['velocity-1.4.csv', 'velocity-1.5.csv', 'velocity-1.6.csv']
Type A: 40%    B: 34%    C: 2%    D: 2%    NoMatch: 20%
Type A: 26%    B: 37%    C: 3%    D: 29%    NoMatch: 2%

['xalan-2.4.csv', 'xalan-2.5.csv', 'xalan-2.6.csv', 'xalan-2.7.csv']
Type A: 9%    B: 4%    C: 36%    D: 44%    NoMatch: 4%
Type A: 27%    B: 20%    C: 15%    D: 31%    NoMatch: 4%
Type A: 44%    B: 0%    C: 51%    D: 1%    NoMatch: 2%

['xerces-1.2.csv', 'xerces-1.3.csv', 'xerces-1.4.csv']
Type A: 3%    B: 11%    C: 10%    D: 72%    NoMatch: 1%
Type A: 7%    B: 0%    C: 38%    D: 25%    NoMatch: 27%

Idea: New dataset consisting of:

• All attributes of N
• All attributes of N+1
• The delta between N and N+1
• Class of defect change

Result1

• Preliminary feature selection with info gain selecting top 50%
• Normalized and discredited with Fayyed-Irani
• PCA via FastMap
• Grid clustering
• Centroids plotted along with version n+1 nearest neighbor lines. (Not terribly useful)
• Do I smell transforms of best fit around the corner?

Results0

k-means 5 to cluster each data-set within itself

Eigenvalues used to determine select features with most influance

Actual selected columns are plotted, not synthesized dimensions

     -- significant correlations could be reported as synonmyms

rules for connecting the dots?