Software analytics and how to draw
conclusions out of data
Presenting the sw-analytics toolkit!
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context
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What are software metrics?
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Which metric is the best?
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LOC, #defects, #contributors, cyclomatic complexity, ...
None?
Can SW metrics be used to draw conclusions about
software?
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sw-analytics toolkit
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Scrapes VCS & issue tracker
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Analyzes everything on function granularity
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Stores in RDBMS
Web-based “Exploratory visualization”
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Subsystem – file - function
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Treemap - Overview
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Treemap - Overview
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Treemap - subsystems
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Treemap - files
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Treemap - functions
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Chart – file & function
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Chart – file & function
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Chart – file & function
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Coming attractions
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Defect predictions
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Automatic redesign/refactoring identification
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Subsystem – file – function
Effort distribution
….
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Defect prediction
Sum of all defects
Defects
30000
20000
2009
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2012
2015
Defect prediction
R2 over time
0.55
0.3
2009
2012
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2015
Defect prediction
per file for 2014
RandomForestRegressor
Ground truth
Predicted
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predicted/truth
conclusions
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No single metric exists that will tell you what
your problem is!
SW-analytics toolkit helps you analyze your
code in a robust way
SW-analytics toolkit have some really cool
features coming up! =)
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SW-Analytics toolkit:
http://www.github.com/jderehag/swat
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