app store cluster analysis

8/18/2019 App Store Cluster Analysis

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Feature Analysis in App Stores Afnan A. AlSubaihin

Feature Analysis in App Stores

Afnan A. Al Subaihin

Supervisors

Mark Harman Sue Black

Licia Capra Federica Sarro

Finding Latent Clustering of Mobile AppsBased on Their Extracted Features


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Motivation

Related Work

Clustering Approach

Future Directions

Feature Extraction

technique

Clustering algorithm and

Distance Metric

Clustering Validation

Feature Representation

Data Acquisition


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Developer User

Select ideal category Easily discover apps

Motivation


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“Sometimes it’s not easy to select a category ifyou app does more than one thing, for example,social music sharing.”

What Do developers Think?

Motivation


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Guess the Category

Motivation


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Does it matter?

“Of course it does! If I put it in ‘social’ where all the

‘big players’ are, I have less chance of being on thetop lists..”


Motivation


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“Categories are not useful in discovery, we try topromote using other means..”

“Categories are too deep and the ‘big players’ are on

the top, we don’t have a chance.”


Motivation



Methods used to find appsSoo Ling Lim et al. Investigating Country Differences in Mobile App User Behavior and Challenges for

Software Engineering

Motivation



App Store App Store

Motivation



Related Work

Motivation

Clustering Approach

Future Directions

Feature Extraction

technique


Distance Metric



Data Acquisition



Related Work



B. Sanz, I. Santos, C. Laorden, X. Ugarte-Pedrero, and P. G. Bringas, “On the automatic categorisation of androidapplications," in 2012 IEEE Consumer Communications and Networking Conference (CCNC), pp. 149-153, IEEE, Jan.2012.

S. Vakulenko, O. Muller, and J. Brocke, “Enriching iTunes App Store Categories via Topic Modeling," in Proceedingsof the Thirty Fifth International Conference on Information Systems (ICIS), (Auckland, New Zealand), 2014.

S. Kawaguchi, P. K. Garg, M. Matsushita, and K. Inoue, “MUDABlue: An automatic categorization system for OpenSource repositories," Journal of Systems and Software, vol. 79, pp. 939-953, July 2006.

K. Tian, M. Revelle, and D. Poshyvanyk, “Using Latent Dirichlet Allocation for automatic categorization ofsoftware," in 2009 6th IEEE International Working Conference on Mining Software Repositories, pp. 163-166, IEEE,May 2009

M. Linares-Vasquez, C. McMillan, D. Poshyvanyk, and M. Grechanik, “On using machine learning to automaticallyclassify software applications into domain categories,” Empirical Software Engineering, vol. 19, pp. 582-618, Oct.2012.

A. Shabtai, Y. Fledel, and Y. Elovici, “Automated Static Code Analysis for Classifying Android Applications UsingMachine Learning," in 2010 International Conference on Computational Intelligence and Security, pp. 329-333, IEEE,Dec. 2010.

T. Wang, H. Wang, G. Yin, C. X. Ling, X. Li, and P. Zou, “Mining Software Profiles across Multiple Repositories forHierarchical Categorization," in 2013 IEEE International Conference on Software Maintenance, pp. 240-249, IEEE,Sept. 2013.



FeatureConceptualisation

Software

Categorisation+



DomainAnalysis FeatureLocation

Feature Conceptualisation

App StoreAnalysis

Feature Model

Synthesis

MaintenanceCode Re-

use

Feature-based

feedback

FeatureRequests

FeatureBehaviour

Monitoring



Developer Benefits

Software Categorisation

AnomalyDetection

User Benefits

ApplicationDiscovery

ApplicationComparison

FacilitatingCode Re-Use

MonitorTechnical

Trends

Find CommonBugs



Clustering Approach

Motivation

Related Work

Future Directions

Feature Extraction

technique


Distance Metric



Data Acquisition



Clustering Approach

Feature Extraction

technique


Distance Metric



Data Acquisition



App Database

2014

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Category Number of Apps

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19/41Feature Analysis in App Stores Afnan A. AlSubaihinFeature Life Cycles in App Stores

SamsungApp Store



choose, photo, automatically

link, Google, drive

list, making

image select

create list automatically

Features Apps

Documents to Go

Photo Sketch HD

Shopping List

Radio Superior

Note+

Total Number of features = 23,337

Clustering Approach


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choose select automatically link list photo image

choose, photo, automatically 1 0 1 0 0 1 0

link, Google, drive 0 0 0 1 0 0 0

list, making 0 0 0 0 1 0 0

image select 0 1 0 0 0 0 1

create list automatically 0 0 1 0 1 0 0

terms

Clustering Approach


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link, Google, drive

list, making

image select


Features


Clustering Approach


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link, Google, drive

list, making

image select


Features


Clustering Approach


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link, Google, drive

list, making

image select


Features


Clustering Approach


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choose, photo, automatically 1 0 1 0 0 1 0

link, Google, drive 0 0 0 1 0 0 0

list, making 0 0 0 0 1 0 0

image select 0 1 0 0 0 0 1

create list automatically 0 0 1 0 1 0 0

terms

!"#$%& ' )*+,-./01 2# 304%-105

,-./01 2# #04%-105 627%4!7!78 %


Clustering Approach


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choose, photo, automatically 1*5 0 1*2.5 0 0 1*5 0

link, Google, drive 0 0 0 1*5 0 0 0

list, making 0 0 0 0 1*2.5 0 0

image select 0 1*5 0 0 0 0 1*5

create list automatically 0 0 1*2.5 0 1*2.5 0 0

terms

!"#$%& ' )*+,-./01 2# 304%-105

,-./01 2# #04%-105 627%4!7!78 %


Clustering Approach


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choose, photo, automatically 0.7 0 0.4 0 0 0.7 0

link, Google, drive 0 0 0 0.7 0 0 0

list, making 0 0 0 0 0.4 0 0

image, select

0 0.7 0 0 0 0 0.7create, list, automatically 0 0 0.4 0 0.4 0 0

terms

!"#$%& ' )*+,-./01 2# 304%-105

,-./01 2# #04%-105 627%4!7!78 %


Clustering Approach


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choose, photo, automatically 0.7 0 0.4 0 0 0.7 0


list, making 0 0 0 0 0.4 0 0

image, select

0 0.7 0 0 0 0 0.7create, list, automatically 0 0 0.4 0 0.4 0 0

terms

!"# $ % &!' #( $


Clustering Approach


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sim(t1,t2) = The length of the

shortest path


Clustering Approach


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choose, photo, automatically 0.7 0.7 0.4 0 0 0.7 0.7


list, making 0 0 0 0 0.4 0 0

image, select

0.7 0.7 0 0 0 0.7 0.7create, list, automatically 0 0 0.4 0 0.4 0 0

terms

!"# $ % &!' #( $


Clustering Approach


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Selecting number of clusters: Can’s Metric

! "#$%&'( *+ ,'-.$('/ 0#$%&'( *+ 1'(%/

#$%&'( *+ 2*2 3 4'(* '2.(5'/ 6 78999

Spherical K-Means

Image courtesy of Christian S. Perone. http://blog.christianperone.com/2013/09/machine-learning-cosine-similarity-for-vector-space-models-part-iii/

Clustering algorithm and Distance Metric

Clustering Approach

http://blog.christianperone.com/2013/09/machine-learning-cosine-similarity-for-vector-space-models-part-iii/


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link, Google, drive 0 0 0 0.7 0 0 0 1

list, making 0 0 0 0 0.4 0 0

create, list, automatically

0 0 0.4 0 0.4 0 0

0.23


image, select 0.7 0.7 0 0 0 0.7 0.70.04


Clustering Approach


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choos

eselect

automatically link list photo image

choose, photo,automaticall

0.7 0 0.4 0 0 0.7 0


list, making 0 0 0 0 0.4 0 0

image, select 0 0.7 0 0 0 0 0.7

create, list, automatically 0 0 0.4 0 0.4 0 0

choos

eselect

automatically link list photo image

choose, photo,automaticall

0.7 0.7 0.4 0 0 0.7 0.7


list, making 0 0 0 0 0.4 0 0

image, select 0.7 0.7 0 0 0 0.7 0.7

create, list, automatically 0 0 0.4 0 0.4 0 0

Adjusted Rand Index = 0.12

-10

10.5-0.5

Exactly the sameExact Disagreement


Clustering Approach


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C

f4f3

f1f2

f4f3

f5C

1

2

f10f35

f40

C3

C

f56

f33

f204

4

f87

f60

FC3FC1

FC2

FC3


Clustering Approach


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FC1 FC2 ..

Documents to Go 1 0 ..

Photo Sketch HD 0 0 ..

Shopping List 0 0 ..

Radio Superior 0 1 ..

Note+ 1 1 ..

App x Feature Vector Space

FC1 FC2


Clustering Approach


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Hierarchical Clustering


Clustering Approach


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! "#$%&'( *+ ,'-.$('/ 0#$%&'( *+ 1'(%/

#$%&'( *+ 2*2 3 4'(* '2.(5'/ 6 788

K-Means where,

Shape: Original Category

Color: Assigned Cluster

Clustering of appsaccording to shared featureclusters. Means where k =368. Mapped using tSNE


Clustering Approach


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Manual: Rate relatedness between every two apps in a cluster

Do clusters show same distribution of app metrics to that found inapp store categories?

Internal Validation

Cluster cohesion (inter similarity), coverage, intra similarity and

silhouette

External Validation

Do apps in different clusters exhibit different tendencies in terms of

app store metrics?

Clustering Evaluation

Clustering Approach


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Future Directions

Motivation

Related Work

Clustering Approach

Feature Extraction

technique


Distance Metric



Data Acquisition


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Better internal, external cluster validation

Employ different feature extraction

Compare with regular text clustering using whole description

Incorporate the source code to enhance clustering

Further tweak approach variables: clustering methods, feature granularity

Future Directions


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