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SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald Gall, Michael Fischer, Michele Lanza: Visualizing multiple evolution metrics

SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

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Page 1: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

SOFTVIS 2005: Saint Louis, Missouri, USA

Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives

Martin Pinzger, Harald Gall, Michael Fischer, Michele Lanza: Visualizing multiple evolution metrics

Page 2: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Data Mining Terminology

• Association rules: Item changed at the same time (related item)

• Sequence rules: order of these changes

• Binary Association Rules: how often 2 items changed together

• Support: Number of transaction containing the item

• Confidence: Number of Changes for pair item over single item

• Outliers: unbalance datasets or abnormal distance

Page 3: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Introduction

• What is visualize

- Binary association rules

- n-ary association rules

- Sequence rules

- distribution, support and confidence –histogram

• Tool EPOSee: Integrates different view

• Purpose: detect clusters, inspect rules, zoom and

filters

Page 4: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

EPOSee InterfacePixelmap

Support Graph

3D Bar Chartfilter

Search keywordColors

Page 5: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Parallel Coordinates View Decision Tree

3D branch view

Page 6: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Rule matrix

Item list

Rule detail Support & confidence

n-ary association rules

Page 7: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

3D bar charts

• Strong dependecies: High Support & confidence

• Use color and heights

Page 8: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Visualize binary association rule only

Pixelmap

File ordering: hierarchical

Page 9: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Stronger related

Pixelmap Example

File coupling atdifferent directorylevel

Page 10: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Edges: related items

Outliers: blue

Clusters: sets of items

Support Graph

Nodes: Items

Red:high

Page 11: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Association Rule Matrix

y-axis: Items

x-axis: Rules

Red, blue & white pixels

Support:length

Confidencecolor

Page 12: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Parallel Coordinates View

Page 13: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

VisualizeSequence

Rules

Parallel CoordinatesView

Nodes Color: Support Values

Edges Color: Confidences

Cluster on samesubdirectory

Page 14: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Parallel Coordinates View

Green edges: high confidence

But, no edges with high confidence is coming into these 2 nodes

Page 15: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Pinzger, Gall, Fischer, Lanza:Visualizing multiple evolution metrics

Page 16: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

• Objective: Communicate the evolution of metrics of source code entities and their relationships

Kiviat Diagram

M1, M2..,M6 = 6 metrics

increasing

decreasing

Page 17: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Metrics

Page 18: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

Logical Coupling

Edge: Coupling relationship

Page 19: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald

A module from Mozilla

Page 20: SOFTVIS 2005: Saint Louis, Missouri, USA Michael Burch, Stephan Diehl, Peter Weißgerber: Visual data mining in software archives Martin Pinzger, Harald