1

Production

2

3

Features Code Changes

FA CA1

FB CB1

4

• If change CA1 implements FA and change CB1 implements FB

Feature Code Changes

FA CA1

FB CB1

Missing Dependencies

CA2 CB1

• If a change CA2 is added to modify

FA and CA2 is dependent on CB1

CA1 CA2 CB1

CB1

CA1

Integrate FA

Integrate FB 5

CA1 CB1 CA2

6

Commit Assignment Algorithm

Automated Grouping ( during

Integration)

Developer Guided Grouping ( during

Development)

Calibrate the Metrics on

Prior Versions

Define Dissimilarity

Metrics

8

Metric Description

File Dependency Distance (FD)

Captures source code dependencies between files involved in two commits

File Association Distance (FA)

Captures logical dependencies between files involved in two commits

Developer Dissimilarity Distance (DD) Captures the working relation between two developers submitting commits

CR Dependency Distance (CRD)

Captures the dissimilarity between the CRs implemented by two commits

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Given two commits characterized by files, developers and change requests (CRs)

Commit Assignment Algorithm

Automated Grouping ( during

Integration)

Developer Guided Grouping ( during

Development)

Calibrate the Metrics on

Prior Versions

Define Dissimilarity

Metrics

10

For each of the four metrics - • Min_Threshold = Avg(a)

• Max_Threshold = Avg(bmin)

• Silhouette= Avg{(bmin-a)/max(bmin,a)}

A higher silhouette value is better

a

b1

b2 b3

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Commit Assignment Algorithm

Automated Grouping ( during

Integration)

Developer Guided Grouping ( during

Development)

Calibrate the Metrics on

Prior Versions

Define Dissimilarity

Metrics

12

Color > Shape

• Apply the similarity metrics in order of their precedence

• If no suitable group is found for a commit, assign the commit to a new group

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Commit Assignment Algorithm

Automated Grouping ( during

Integration)

Developer Guided Grouping ( during

Development)

Calibrate the Metrics on

Prior Versions

Define Dissimilarity

Metrics

14

15

Groups commits incrementally and uses developers’ feedback to improve the grouping during development

Both approaches follow the k-means clustering method which consists in assigning each item to the cluster with the nearest mean.

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We analyzed three major versions of a family of mobile applications

• Validate the dissimilarity metrics

Can the proposed metrics be used to identify commit dependencies ?

• Validate the grouping approaches

How efficient are our proposed grouping approaches?

• Value for Developers

Can the proposed approaches identify commit dependencies missed by developers ?

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The four similarity metrics display good abilities in grouping commits ( i.e. high silhouette values)

0.94 0.96 0.96

0.76 0.79

0.67 0.63

0.67

0.57

0.46 0.47 0.49

0

0.2

0.4

0.6

0.8

1

Verion 1 Version 2 Version 3

CRD FA DD FD

Silh

ouet

te V

alue

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CRD > FA > DD > FD

• Efficiency of the Grouping Approaches – 82% of commit dependencies were recovered by

the automated grouping with a precision of 95% – The accuracy of the developer-guided grouping

approach is 98% – We observed that precision/recall improves with

longer history data • Value for Developers

– Automated grouping and Developer-guided grouping approaches were able to reduce integration failures by 76% and 94% respectively

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