Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps

Khalid Alharbi; Tom Yeh

doi:10.1145/2785830.2785892

Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps

Khalid Alharbi, Tom Yeh

Department of Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

Mobile user interface design patterns have been widely used across different mobile platforms. UI design patterns have evolved and changed significantly as new trends emerge and fade at different times. This paper presents a data-mining approach to analyzing design pattern changes in Android apps. Over a period of 18 months, we tracked 24,436 apps and collected their versions. In total, our sample consists of 56,349 unique app versions, more than 5 million source files, and more than 25 million UI elements. We developed a dedicated infrastructure based on modern big data technologies to support our differential analyses regarding design pattern changes. Some highlights of our findings include a) some apps would switch to a design pattern even after it was deprecated, b) the adoption rate of newly introduced design patterns (e.g., Fragment) is relatively low, c) some apps would update their listing details to reflect changes in design patterns.

Original language	English
Title of host publication	MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
Publisher	Association for Computing Machinery, Inc
Pages	515-524
Number of pages	10
ISBN (Electronic)	9781450336529
DOIs	https://doi.org/10.1145/2785830.2785892
State	Published - 24 Aug 2015
Event	17th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2015 - Copenhagen, Denmark Duration: 24 Aug 2015 → 27 Aug 2015

Publication series

Name	MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

Conference

Conference	17th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2015
Country/Territory	Denmark
City	Copenhagen
Period	24/08/15 → 27/08/15

Bibliographical note

Publisher Copyright:
© 2015 ACM.

Keywords

Analysis
Android
Apps
Design
Pattern
UI

Access to Document

10.1145/2785830.2785892

Cite this

Alharbi, K., & Yeh, T. (2015). Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps. In MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (pp. 515-524). (MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services). Association for Computing Machinery, Inc. https://doi.org/10.1145/2785830.2785892

Alharbi, Khalid ; Yeh, Tom. / Collect, decompile, extract, stats, and diff : Mining design pattern changes in android apps. MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. Association for Computing Machinery, Inc, 2015. pp. 515-524 (MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services).

@inproceedings{7f9bf3772967485e8793b60cba7f84e2,

title = "Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps",

abstract = "Mobile user interface design patterns have been widely used across different mobile platforms. UI design patterns have evolved and changed significantly as new trends emerge and fade at different times. This paper presents a data-mining approach to analyzing design pattern changes in Android apps. Over a period of 18 months, we tracked 24,436 apps and collected their versions. In total, our sample consists of 56,349 unique app versions, more than 5 million source files, and more than 25 million UI elements. We developed a dedicated infrastructure based on modern big data technologies to support our differential analyses regarding design pattern changes. Some highlights of our findings include a) some apps would switch to a design pattern even after it was deprecated, b) the adoption rate of newly introduced design patterns (e.g., Fragment) is relatively low, c) some apps would update their listing details to reflect changes in design patterns.",

keywords = "Analysis, Android, Apps, Design, Pattern, UI",

author = "Khalid Alharbi and Tom Yeh",

note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2015 ; Conference date: 24-08-2015 Through 27-08-2015",

year = "2015",

month = aug,

day = "24",

doi = "10.1145/2785830.2785892",

language = "English",

series = "MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services",

publisher = "Association for Computing Machinery, Inc",

pages = "515--524",

booktitle = "MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services",

}

Alharbi, K & Yeh, T 2015, Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps. in MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, Association for Computing Machinery, Inc, pp. 515-524, 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2015, Copenhagen, Denmark, 24/08/15. https://doi.org/10.1145/2785830.2785892

Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps. / Alharbi, Khalid; Yeh, Tom.
MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. Association for Computing Machinery, Inc, 2015. p. 515-524 (MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Collect, decompile, extract, stats, and diff

T2 - 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2015

AU - Alharbi, Khalid

AU - Yeh, Tom

PY - 2015/8/24

Y1 - 2015/8/24

N2 - Mobile user interface design patterns have been widely used across different mobile platforms. UI design patterns have evolved and changed significantly as new trends emerge and fade at different times. This paper presents a data-mining approach to analyzing design pattern changes in Android apps. Over a period of 18 months, we tracked 24,436 apps and collected their versions. In total, our sample consists of 56,349 unique app versions, more than 5 million source files, and more than 25 million UI elements. We developed a dedicated infrastructure based on modern big data technologies to support our differential analyses regarding design pattern changes. Some highlights of our findings include a) some apps would switch to a design pattern even after it was deprecated, b) the adoption rate of newly introduced design patterns (e.g., Fragment) is relatively low, c) some apps would update their listing details to reflect changes in design patterns.

AB - Mobile user interface design patterns have been widely used across different mobile platforms. UI design patterns have evolved and changed significantly as new trends emerge and fade at different times. This paper presents a data-mining approach to analyzing design pattern changes in Android apps. Over a period of 18 months, we tracked 24,436 apps and collected their versions. In total, our sample consists of 56,349 unique app versions, more than 5 million source files, and more than 25 million UI elements. We developed a dedicated infrastructure based on modern big data technologies to support our differential analyses regarding design pattern changes. Some highlights of our findings include a) some apps would switch to a design pattern even after it was deprecated, b) the adoption rate of newly introduced design patterns (e.g., Fragment) is relatively low, c) some apps would update their listing details to reflect changes in design patterns.

KW - Analysis

KW - Android

KW - Apps

KW - Design

KW - Pattern

KW - UI

UR - http://www.scopus.com/inward/record.url?scp=84959419451&partnerID=8YFLogxK

U2 - 10.1145/2785830.2785892

DO - 10.1145/2785830.2785892

M3 - Conference contribution

AN - SCOPUS:84959419451

T3 - MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

SP - 515

EP - 524

BT - MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

PB - Association for Computing Machinery, Inc

Y2 - 24 August 2015 through 27 August 2015

ER -

Alharbi K, Yeh T. Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps. In MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. Association for Computing Machinery, Inc. 2015. p. 515-524. (MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services). doi: 10.1145/2785830.2785892

Collect, decompile, extract, stats, and diff: Mining design pattern changes in android apps

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this