1. PRADA: Prioritizing Android
Devices for Apps by Mining
Large-Scale Usage Data
Presented by: Akshay Mittal
Course: CS 5393
Professor: Dr. Guowei Yang
5. Motivation
• Android Fragmentation that is,
concern over the alarming
number of different
available Android operating
system (OS) versions in the
market.
• to explore whether we can
make accurate estimates for
a new app.
10/20/2016 CS5393 Software Quality 5
10. Time-Share Driven Prioritization
• Browsing time on app • Collaborative Filtering by Time
Share
It Uses, Leave-One-Out Cross-
Validation (LOOCV)
10/20/2016 CS5393 Software Quality 10
11. Algorithm-I
Device model hit (DH), time
share coverage (T C) and
average precision (AP) against
top N device models with K
apps in the same category
10/20/2016 CS5393 Software Quality 11
12. Evaluation
1. Device Model Distribution
• Explains, RQ1: How many
device models account for
the majority of the browsing
time?
• Uses, Pareto Principle
2. Predicting Top Device Model
• Explains, RQ2: How effectively
can PRADA identify major
device models for a new app
given that developers have
no knowledge about this
app’s actual usage?
10/20/2016 CS5393 Software Quality 12
13. Device Model Distribution Statistics
10/20/2016 CS5393 Software Quality 13
Number of device models and users that use top 100 apps
from each of the two categories.
14. Results from Predicting Top Device Models
Using collaborative filtering algorithm on Game and Media Apps
10/20/2016 CS5393 Software Quality 14
Top 10 device models with the most time share for two apps (Temple Run 2 and Xunlei Movie),
and the selected device models by AppBrain, Wandoujia, and PRADA.
15. Comparison of Device Model Hit, Time Share Coverage, and AP by using market share
and PRADA to recommend top 10 device models for Game apps
Results of Device Model Hit, Time Share Coverage, and AP of top 10 device
models that are predicted by PRADA for 100 apps in each category, i.e., N =10
and K=100.
10/20/2016 CS5393 Software Quality 15
16. Strength of Prada
10/20/2016 CS5393 Software Quality 16
Mining from large scale of data
Leverage usage data
Satisfactory accuracy
Operational Profiling
17. Weakness
• Restricted on only two network categories
• Need access to existing data usage
• Not accurate for offline apps
• Relies on accuracy of Wandoujia
10/20/2016 CS5393 Software Quality 17
18. Related work
• A framework for detecting similar mobile applications by online
kernel learning.
• Rescaling reliability bounds for a new operational profile
• Mining large-scale smartphone data for personality studies.
• Prioritizing the devices to test your app on: A case study of
Android game apps.
• Understanding Android fragmentation with topic analysis of
vendor-specific bugs.
10/20/2016 CS5393 Software Quality 18
19. Conclusion
PRADA includes a collaborative filtering
technique to accurately predict major
device models for a new app, given the
usage data from existing apps with similar
functionalities.
Future work a) impact of localization on
device model prioritization.
b) how to cluster device models at different
granularities.
10/20/2016 CS5393 Software Quality 19
20. Questions for Deeper Analysis
• How the system can be efficient without
the time-share-based technique not
included in Wandoujia dataset?
• Why only browsing time is main parameter
in analysis?
10/20/2016 CS5393 Software Quality 20
