Modeling History to Understand Software Evolution with Hismo 2008-03-12

Modeling Historyto Understand Software Evolution

Tudor Gîrbawww.tudorgirba.com

http://www.tudorgirba.com


to Understand Software Evolution

Modeling History

vorgelegt von

Tudor Gîrba

von Rumänien

Inauguraldissertation der Philosophisch-naturwissenschaftlichen

Fakultät der Universität Bern

Leiter der Arbeit:

Prof. Dr. Stéphane DucasseProf. Dr. Oscar Nierstrasz

Institut für Informatik und angewandte Mathematik

http://www.tudorgirba.com/publications



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Lehman etal, 2001

Most often time is put on the horizontaland a property on the vertical axis.

Lanza, Ducasse, 2002

Evolution Matrix shows how classes evolve.Time is still on the horizontal axis.

Gall etal, 2003

Co-change analysis recovers hidden dependencies.Time is the lines.

Eick etal, 2002

Evolution information can bemapped on structural information.

Lehman etal, 2001

Eick etal, 2002

Lanza, Ducasse, 2002Gall etal, 2003

...

Lehman etal, 2001

Eick etal, 2002


...How can we accommodate all t

hese techniques?

short intermezzo

What is a model?

short intermezzoA model is a simplification of the subject,

and its purpose is to answer some particular

questions aimed towards the subject.

Bezivin, Gerbe, 2001

short intermezzo

what is a meta-model?

short intermezzoa meta-model isa model that makes statements aboutwhat can be expressed in valid models.

Seidewitz, 2003

short intermezzoa good meta-modelallows for succinctexpression of analyses.

Lehman etal, 2001

Eick etal, 2002


...

Lehman etal, 2001

Eick etal, 2002


...How can we accommodate all t

hese techniques?

Evolution Matrix shows class changes.

Idleclass

Pulsarclass

Supernovaclass

White dwarfclass Class

attributes

methods



Idleclass

Pulsarclass

Supernovaclass


attributes

methods

Evolution needs to be modeled as first c

lass entity.



Idleclass

Pulsarclass

Supernovaclass


attributes

methods



Idleclass history

Pulsarclass history

Supernovaclass history

White dwarfclass history

ClassHistory

isPulsarisIdle...

ClassVersion

SystemVersion

ClassVersion

ClassHistory

SystemVersion

ClassVersion

ClassHistory

SystemVersion

SystemHistory

ClassVersion

ClassHistory

SystemVersion

SystemHistory

Hismo models history as first c

lass entity.

Girba, 2005

1 2 3

4 5 6

Measuringhistory

Yesterday’sWeather

Time-basedDetection Strategies

Visualizing the evolution of hierarchies

Detectingco-change patterns

How developersdrive evolution

1Measuring history

2 4 3 5 7

2 2 3 4 9

2 2 1 2 3

2 2 2 2 2

1 5 3 4 4

What changed? When did it change? ...

1 5 3 4 4

4 2 1 0+++ = 7=

LENOM(C) = ∑ |NOMi(C)-NOMi-1(C)| 2i-nEvolution ofNumber of Methods

LENOM(C)

1 5 3 4 4

LENOM(C) = ∑ |NOMi(C)-NOMi-1(C)| 2i-n

LENOM(C) 4 2-3 2 2-2 1 2-1 0 20+++ = 1.5=

EENOM(C) = ∑ |NOMi(C)-NOMi-1(C)| 22-i

Latest Evolution ofNumber of Methods

Earliest Evolution ofNumber of Methods

EENOM(C) 4 20 2 2-1 1 2-2 0 2-3+++ = 5.25=

ENOM LENOM EENOM

7 3.5 3.25

7 5.75 1.37

3 1 2

0 0 0

7 1.25 5.25

2 4 3 5 7

2 2 3 4 9

2 2 1 2 3

2 2 2 2 2

1 5 3 4 4

ENOM LENOM EENOM

7 3.5 3.25

7 5.75 1.37

3 1 2

0 0 0

7 1.25 5.25

balanced changer

late changer

dead stable

early changer

ENOM LENOM EENOM

7 3.5 3.25

7 5.75 1.37

3 1 2

0 0 0

7 1.25 5.25

balanced changer

late changer

dead stable

early changer

History as first c

lass entity

enables comparison through measurements.

Evolution

Stability

Historical Max

Growth Trend

...

Number of Methods

Number of Lines of Code

Cyclomatic Complexity

Number of Modules

...

of

History can be measured in many ways.

2Yesterday’s weather

The recently changed parts are likely to change in the near future.

Common wisdom

The recently changed parts are likely to change in the near future.

Common wisdom

Are they really?

30% 90%

present

present

past

present

past future

present

past future

prediction hit

present

past future

YesterdayWeatherHit(present):

past:=histories.topLENOM(start, present) future:=histories.topEENOM(present, end)

past.intersectWith(future).notEmpty()

prediction hit

Overall Yesterday’s Weather shows the localization of changed in time. Girba etal, 2004

hit hit hit

YW = 3 / 8 = 37%

hit hit hit hit hit hit hit

YW = 7 / 8 = 87%

3Time-based Detection Strategies

Detection Strategies are metric-based queries to detect design flaws.

METRIC 1 > Threshold 1

Rule 1

METRIC 2 < Threshold 2

Rule 2

AND Quality problem

Lanza, Marinescu 2006

Example: a God Class centralizes too much intelligence in the system.

ATFD > FEW

Class uses directly more than a

few attributes of other classes

WMC ! VERY HIGH

Functional complexity of the

class is very high

TCC < ONE THIRD

Class cohesion is low

AND GodClass

Lanza, Marinescu, 2006

Example: a God Class centralizes too much intelligence in the system.

ATFD > FEW

Class uses directly more than a

few attributes of other classes

WMC ! VERY HIGH

Functional complexity of the

class is very high

TCC < ONE THIRD

Class cohesion is low

AND GodClass

Lanza, Marinescu, 2006

But, what if it is

stable?

History-based Detection Strategies take evolution into account. Ratiu etal, 2004

AND

isGodClass(last)

God Class

in the last version

Stability > 90%

Stable throughout

the history

Harmless God Class

History-based Detection Strategies take evolution into account. Ratiu etal, 2004

AND

isGodClass(last)

God Class

in the last version

Stability > 90%

Stable throughout

the history

Harmless God Class

Time and space are treated the same.

4Visualizing the evolution of hierarchies

What happens with inheritance?

ver .1 ver. 2 ver. 3 ver. 4 ver. 5

A A A A A

B B B B BC C C

D D D E

History contains too much data.


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E


A A A A A

B B B B BC C C

D D D E

ClassVersion

ClassHistory

SystemVersion

SystemHistory

ClassVersion

ClassHistory

SystemVersion

SystemHistory

InheritanceVersion

InheritanceHistory

ClassVersion

ClassHistory

SystemVersion

SystemHistory

InheritanceVersion


A A A A A

B B B B BC C C

D D D E

A is persistent, B is stable, C was removed, E is newborn ...

Hierarchy Evolution View encapsulates time.

A

B

D

C

E

A is persistent, B is stable, C was removed, E is newborn ...

age

changedmethods

changedlines

Removed

Removed

Girba etal, 2005

Hierarchy Evolution View reveals patterns.Girba etal, 2005

Hierarchy Evolution View reveals patterns.Girba etal, 2005

History as first c

lass entity

enables mapping to a graph.

5Identifying co-change patterns

A

B

C

D

E

1 2 3 4 5 6

B E

C D

A

Gall etal, ‘98

Co-change patterns are n-ary relationships.

A

B

C

D

E

1 2 3 4 5 6

Version

A

B

C

D

E

1 2 3 4 5 6

changed

Version

changed(i)

HistoryA

B

C

D

E

1 2 3 4 5 6

changed

Version

What is Concept Analysis?

A

B

C

D

E

1 2 3 4 5 6

{A, B, C, D, E}

Ø

{D, B}{2, 4}

{A, D}{2, 6}

{A, E, C}{5, 6}

{A, D, B}{2}

{A, E, C, D}{6}

{D}{2, 4, 6}

{A}{2, 5, 6}

{C}{3, 5, 6}

Ø{1, 2, 3, 4, 5, 6}

FCA

A

B

C

D

E

1 2 3 4 5 6

{A, B, C, D, E}

Ø

{D, B}{2, 4}

{A, D}{2, 6}

{A, E, C}{5, 6}

{A, D, B}{2}

{A, E, C, D}{6}

{D}{2, 4, 6}

{A}{2, 5, 6}

{C}{3, 5, 6}

Ø{1, 2, 3, 4, 5, 6}

FCA

A

B

C

D

E

1 2 3 4 5 6

Girba etal, 2007

Parallel Inheritanceadd simultaneously children to several classes

Shotgun Surgerychange several classes simultaneously, but do not add methods

{A, B, C, D, E}

Ø

{D, B}{2, 4}

{A, D}{2, 6}

{A, E, C}{5, 6}

{A, D, B}{2}

{A, E, C, D}{6}

{D}{2, 4, 6}

{A}{2, 5, 6}

{C}{3, 5, 6}

Ø{1, 2, 3, 4, 5, 6}

FCA

A

B

C

D

E

1 2 3 4 5 6

{A, B, C, D, E}

Ø

{D, B}{2, 4}

{A, D}{2, 6}

{A, E, C}{5, 6}

{A, D, B}{2}

{A, E, C, D}{6}

{D}{2, 4, 6}

{A}{2, 5, 6}

{C}{3, 5, 6}

Ø{1, 2, 3, 4, 5, 6}

FCA

A

B

C

D

E

1 2 3 4 5 6

History as first c

lass entity

enables mapping to FCA.

6How developers drive software evolution

CVS shows activity.

Who is responsible for this?

Alphabetical order is no order.

The Hausdorf metric can be used to compute the similarity between commits.

A

B

d(A, B) = ∑ min2{ | a - b | b in B }a in A

Alphabetical order is no order.

Ownership Map reveals development patterns.Girba etal, 2006

Ownership Map reveals development patterns.Girba etal, 2006

History as first c

lass entity

enables reasoning about holistic changes.

1 2 3

4 5 6

Measuringhistory

Yesterday’sWeather

Time-basedDetection Strategies

Visualizing the evolution of hierarchies

Detectingco-change patterns

How developersdrive evolution

InheritanceHistory

ClassVersion

ClassHistory

SystemVersion

SystemHistory

InheritanceVersion

History

VersionHistory

VersionHistory

Version

History

VersionHistory

VersionHistory

Version

Hismo models history as first c

lass entity.

Girba, 2005

to Understand Software Evolution

Modeling History

vorgelegt von

Tudor Gîrba

von Rumänien

Inauguraldissertation der Philosophisch-naturwissenschaftlichen

Fakultät der Universität Bern

Leiter der Arbeit:

Prof. Dr. Stéphane DucasseProf. Dr. Oscar Nierstrasz

Institut für Informatik und angewandte Mathematik

www.tudorgirba.com





Tudor Gîrbawww.tudorgirba.com

creativecommons.org/licenses/by/3.0/



http://creativecommons.org/licenses/by/3.0/

http://creativecommons.org/licenses/by/3.0/

Modeling History to Understand Software Evolution with Hismo 2008-03-12

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