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Software analytics - and how to draw conclusions out of data
Presenting the sw-analytics toolkit!
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context What are software metrics?
LOC, #defects, #contributors, cyclomatic complexity, ...
Which metric is the best?
None?
Can SW metrics be used to draw conclusions about software?
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sw-analytics toolkit
Scrapes VCS & issue tracker Analyzes everything on function granularity
Stores in RDBMS
Web-based “Exploratory visualization” 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
Defect predictions Subsystem – file – function
Automatic redesign/refactoring identification Effort distribution
….
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Defect predictionSum of all defects
30000
Defects
20000
2009 20152012
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Defect predictionR2 over time
0.3
0.55
2009 20122009 2015
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Defect predictionper file for 2014
RandomForestRegressor
Ground truth Predicted predicted/truth
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conclusions
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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