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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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