Tracking the Software Quality of Android Applications ... · Tracking the Software Quality of...

Tracking the Software Quality of Android Applicationsalong their Evolution

Geoffrey Hecht, Omar Benomar, Romain Rouvoy, Naouel Moha,Laurence Duchien

UQAM/Université Lille 1/Inria

11/11/2015 (ASE 2015, Lincoln, Nebraska)

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How to track quality ?

Plan

1 How to track quality ?

2 What are antipatterns ?

3 Why android antipatterns ?

4 The Paprika approach

5 Empirical Study

6 Future works

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Tracking QualityBugs

MetricsPerformanceChange-pronenessAntipatterns

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Tracking QualityBugsMetrics

PerformanceChange-pronenessAntipatterns

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Tracking QualityBugsMetricsPerformance

Change-pronenessAntipatterns

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Tracking QualityBugsMetricsPerformanceChange-proneness

Antipatterns

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Tracking QualityBugsMetricsPerformanceChange-pronenessAntipatterns

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What are antipatterns ?

Plan





5 Empirical Study

6 Future works

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AntipatternsBad pratices

Impede maintenanceDegrade performance

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AntipatternsBad praticesImpede maintenance

Degrade performance

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AntipatternsBad praticesImpede maintenanceDegrade performance

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Example : Blob

System

InputHandler

Driver

UI

Entity

Utils

SubSystem

Manager

View

Theme

Analyzer

Database

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Why android antipatterns ?

Plan





5 Empirical Study

6 Future works

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Mobile app specifity (battery, network...)

Android frameworkQuality and performance matterLack of studies

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Mobile app specifity (battery, network...)Android framework

Quality and performance matterLack of studies

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Mobile app specifity (battery, network...)Android frameworkQuality and performance matter

Lack of studies

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Mobile app specifity (battery, network...)Android frameworkQuality and performance matterLack of studies

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Useful for

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Example of Android antipattern : UI Overdraw

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The Paprika approach

Plan





5 Empirical Study

6 Future works

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A four-step approach

GraphDB

APK

Paprikamodel

Metadata

Step 1 : Model Generation

Constructmodel

Extractmetrics

Step 3 : Antipattern Detection

Determinethresholds

Executequeries

Graphmodel

Antipatternqueries

Detectedantipatterns

Step 2 : Model Conversion

Convertentities

Convertmetrics

Step 4 : Software QualityEvolution Computation

Buildestimationmodel

Computequalityscore

Softwarequaityevolution

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Step 1-2

GraphDB

APK

Paprikamodel

Metadata

Step 1 : Model Generation

Constructmodel

Extractmetrics

Graphmodel

Step 2 : Model Conversion

Convertentities

Convertmetrics

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A graph model

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Step 3

GraphDB

Step 3 : Antipattern Detection

Determinethresholds

Executequeries

Graphmodel

Antipatternqueries


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Detected antipatterns

Blob Class (BLOB)

Complex Class (CC)Long Method (LM)Member Ignoring Method (MIM)UIO Overdraw (UIO)Leaking Inner Class (LIC)Heavy Broadcast Receiver (HBR)

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Blob Class (BLOB)Complex Class (CC)

Long Method (LM)Member Ignoring Method (MIM)UIO Overdraw (UIO)Leaking Inner Class (LIC)Heavy Broadcast Receiver (HBR)

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Blob Class (BLOB)Complex Class (CC)Long Method (LM)

Member Ignoring Method (MIM)UIO Overdraw (UIO)Leaking Inner Class (LIC)Heavy Broadcast Receiver (HBR)

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Blob Class (BLOB)Complex Class (CC)Long Method (LM)Member Ignoring Method (MIM)

UIO Overdraw (UIO)Leaking Inner Class (LIC)Heavy Broadcast Receiver (HBR)

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Blob Class (BLOB)Complex Class (CC)Long Method (LM)Member Ignoring Method (MIM)UIO Overdraw (UIO)

Leaking Inner Class (LIC)Heavy Broadcast Receiver (HBR)

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Blob Class (BLOB)Complex Class (CC)Long Method (LM)Member Ignoring Method (MIM)UIO Overdraw (UIO)Leaking Inner Class (LIC)

Heavy Broadcast Receiver (HBR)

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Blob Class (BLOB)Complex Class (CC)Long Method (LM)Member Ignoring Method (MIM)UIO Overdraw (UIO)Leaking Inner Class (LIC)Heavy Broadcast Receiver (HBR)

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MIM Query

MATCH (m :Method)WHERE NOT HAS(m.is_static)AND NOT HAS(m.is_init)AND NOT m-[ :USES]->( :Variable)AND NOT (m)-[ :CALLS]->( :Method)RETURN m

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Step 4


Step 4 : Software QualityEvolution Computation

Buildestimationmodel

Computequalityscore

Softwarequaityevolution

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Example of residuals for linear regression used for quality estimation

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Study

Plan





5 Empirical Study

6 Future works

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Study

Research questionsRQ1 : Can we infer software quality evolution trends across differentversions of Android applications ?

RQ2 : How does software quality evolve in Android applications ?

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Study

Research questionsRQ1 : Can we infer software quality evolution trends across differentversions of Android applications ?RQ2 : How does software quality evolve in Android applications ?

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Study

Dataset3568 versions of 106 apps

Top 150 of each category on Google PlayMinimum 20 versions (June 2013-2014)

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Study

Dataset3568 versions of 106 appsTop 150 of each category on Google Play

Minimum 20 versions (June 2013-2014)

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Study

Dataset3568 versions of 106 appsTop 150 of each category on Google PlayMinimum 20 versions (June 2013-2014)

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Study

APP_W

ALL

PA

PER

BO

OK

S_A

ND

_REFE

REN

CE

BU

SIN

ESS

CO

MIC

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ICA

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N

ED

UC

ATIO

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EN

TER

TA

INM

EN

T

FIN

AN

CE

GA

ME

HEA

LTH

_AN

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ESS

LIFE

STYLE

MED

IA_A

ND

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EO

MED

ICA

L

MU

SIC

_AN

D_A

UD

IO

NEW

S_A

ND

_MA

GA

ZIN

ES

PER

SO

NA

LIZ

ATIO

N

PH

OTO

GR

APH

Y

PR

OD

UC

TIV

ITY

SH

OPPIN

G

SO

CIA

L

SPO

RTS

TO

OLS

TR

AV

EL_

AN

D_L

OC

AL

WEA

TH

ER

category

0

5

10

15

20#

app

Number of apps per category

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Study

10

00

0

50

00

0

10

00

00

50

00

00

10

00

00

0

50

00

00

0

10

00

00

00

50

00

00

00

10

00

00

00

0

50

00

00

00

0

# downloads

0

500

1000

1500

2000

2500

3000

3500

4000

4500avera

ge #

cla

sses

Average number of classes per download categories

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Study

RQ1RQ1 : Can we infer software quality evolution trends across differentversions of Android applications ?

5 trends

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Study

RQ1RQ1 : Can we infer software quality evolution trends across differentversions of Android applications ?5 trends

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Study

Constant Decline (LM)

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

version

800

700

600

500

400

300

200

qualit

y s

core

com.picsart.studio

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Study

Constant Rise (CC)

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

version

36

38

40

42

44

46

48

50

52

qualit

y s

core

flipboard.app

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Study

Stability (LIC)

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

version

120

100

80

60

40

20

0

20

qualit

y s

core

org.mozilla.firefox

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Study

Sudden Decline (BLOB)

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

version

75

70

65

60

55

50

qualit

y s

core

com.evernote

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Study

Sudden Rise (LIC)

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

version

20

40

60

80

100

120

qualit

y s

core

com.skype.raider

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Study

Other observations

20

40

60

80

100

120

140

160C

C

0

200

400

600

800

1000

1200

MIM

0 5 10 15 20 25 30 35version

100

200

300

400

500

600

700

800

900

LM

0 5 10 15 20 25 30 35version

0

10

20

30

40

50

BLO

B

com.imo.android.imoim

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Study

Other observations

500

400

300

200

100

0

100

200LM

0 5 10 15 20 25version

0

10

20

30

40

50

60

BLO

Bcom.onemanwithcameralomo

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Study

RQ2RQ2 : How does software quality evolve in Android applications ?

Case Study of Twitter

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Study

RQ2RQ2 : How does software quality evolve in Android applications ?Case Study of Twitter

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Study

Other observations

0 1 2 3 4 5 6 7 8 91

01

11

21

31

41

51

61

71

81

92

02

12

22

32

42

52

62

72

82

93

03

13

23

33

43

53

63

73

83

94

04

14

24

34

44

54

64

74

84

95

05

15

25

35

45

55

65

75

85

96

06

16

26

36

46

56

66

76

86

97

07

17

27

37

47

5

version

0

1000

2000

3000

4000

5000

# c

lass

es

com.twitter.android

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Study

CC score

0 1 2 3 4 5 6 7 8 91

01

11

21

31

41

51

61

71

81

92

02

12

22

32

42

52

62

72

82

93

03

13

23

33

43

53

63

73

83

94

04

14

24

34

44

54

64

74

84

95

05

15

25

35

45

55

65

75

85

96

06

16

26

36

46

56

66

76

86

97

07

17

27

37

47

5

version

25

20

15

10

5

0

5

10

15

20

qualit

y s

core

com.twitter.android

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Future works

Plan





5 Empirical Study

6 Future works

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Future works

Future worksEvolution of external attributes (app reviews)

Impact on performance

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Future works

Future worksEvolution of external attributes (app reviews)Impact on performance

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Future works

Thank you for your attention

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