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Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2178
ABSTRACT
Design patterns help software developers in building better
software designs as well as fostering software maintainability
and re-usability. Recently, mobile applications, apps
hereinafter, have gained much ground in critical domains,
such as banking, health, payments, and military, just to
mention a few. Accordingly, it has become imperative to
consider increasing the apps' code quality, which urges to
better usage of design patterns. Although there is plethora of
studies that discuss design patterns usage in object-oriented
languages such as Java, C++, and C#, to our best of
knowledge, no studies have discussed design patterns usage
for Android apps. This study performs an exploratory research
using static code analysis methods and a sample of more than
1400 Android apps toward finding design patterns
implemented inside their source code. We extend our PatRoid
framework, which detects all design patterns in Android app
source code. Our initial results show that there is a variation
in the extent in which design patterns are applied among
different Android apps’ categories. Overall, we argue that
there is still a lack of proper usage of design patterns in
Android apps development.
Key words: Android Apps, Design patterns, PatRoid, Static
Analysis, Code Analysis.
1. INTRODUCTION
In a recent statistical comparison between different mobile
platforms, it is apparent that Android platform leads the
market with a significant difference, and is expected to
maintain this position for the upcoming years [2]. Statistics in
the beginning of 2020 show that Android OS holds 86.6% of
total world OS shipment market, while iOS from Apple comes
in second place holding the remaining 13.4%.
Android platform is not limit to mobile phone users only, in
fact, manufacturing industry and software development
community adopted Android in other contexts such as smart
TVs, tablets, wearables, automobiles, etc.[1], [3], [4].
Nowadays, Google Play[5]the official Android apps store,
holds over than 2.1 million apps divided over 60 different
categories.
Our review to the literature, shows that there is a high
diversity of applications for Android apps. They have become
part of complex and critical categories, such as Medical,
Health Monitoring, Banking, Education, Traveling, etc.
Furthermore, each of these categories are discussed by several
studies[6]–[11].
Android app development has special characteristics than
traditional desktop, and web development. Since desktop and
web apps development are considered mature, Android
development is still considered a new field, with a large
portion of developers are known to be novice [1], [12], [13].
That been said, firm guidance is required, and software design
patterns can achieve better software quality, re-usability,
maintainability, and evolution[14]–[19].
Recent and old studies have investigated design patterns
importance, as well as the different approaches to detect them
and how to improve these approaches with different Object
Oriented (O-O) desktop languages, such as Java, C++, and C#
[20]–[27].
On the other hand, the current state-of-the-art shows an
apparent gap in the area of mobile apps. In fact, little studies
were published to address design patterns with Android apps
[19], [28], [29], however, they only investigate UI (User
Interface) design patterns, and not O-O design patterns, which
are the focus of this study.
To the best of our knowledge the only study that addresses
design patterns detection for Android apps is our previous
study [1]. In our previous study, we implemented a new open
source automated framework for design patterns detection in
Android apps (PatRoid1).
PatRoid is a model based on graph isomorphism approach.
Where it divides each design patterns into smaller easy to
catch sub-patterns. PatRoid is capable to detect all 23 Gang of
Four (GoF) design patterns.
This study aims to explore what design patterns do Android
apps developers apply. It extends our previous study to study
a sample of more than 1400 Android apps’ source code
collected from F-Droid2.
Our preliminary results show that Android app developers are
applying O-O design patterns in varying extents depending on
apps categories. Additionally, it shows that the usage of
1PatRoid: is an open source framework for Android O-O design patterns
detection, it is implemented using Python language and can be found at
GitHub on the following link “https://github.com/dmrimawi/PatRoid”.
2F-Droid, a free and open source Android apps repository. It can be found
at F-Droid website “https://f-droid.org/en/”.
A Static Analysis of Android Source Code for
Design Patterns Usage
Diaeddin Rimawi
1
,
Samer Zein
2
1Master of Software Engineering, Birzeit University, Palestine, dmrimawi@gmail.com
2Master of Software Engineering, Birzeit University, Palestine, szain@birzeit.edu
ISSN 2278
-
3091
Volume 9 No.2, March - April 2020
International Journal of Advanced Trends in Computer Science and Engineering
Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse194922020.pdf
https://doi.org/10.30534/ijatcse/2020/194922020
Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2179
design patterns in general in Android apps is insufficient.
On the other hand, our study shows that there is high diversity
over different Android apps categories which means that there
are some categories that are more mature than others, such as
the Tools and Lifestyle categories.
The rest of this paper is structured to first discusses the
literature review in section 2, then illustrates the methodology
in section 3. Section 4 shows the results of this study, and
section 5 discusses these results. Threats to validity and future
work are shown in section 6 and section 7, and finally section
8concludes this study.
2. LITERATURE REVIEW
Static code analysis has been a hot topic for researchers to
achieve several objectives, such as exposing the source code
flaws, testing, privacy, and security investigation.
Li et al. [4] showed that there is a research trend of static
analysis in Android apps specially in security, privacy, and
testing fields. More specifically, in their study they identified
eight topics the literature has been focusing on a) Private Data
Leaks, b) Vulnerabilities, c) Permission Misuse, d) Energy
Consumption, e) Clone Detection, f) Test Cases Generation,
g) Code Verification, and h) Cryptography Implementation
Issues, like [30]–[44]. Moreover, recent studies analyzed
Android source code from different aspects, like apps
lifecycle[12], [13], and redundant apps detection [45].
Researchers started studying O-O design patterns for a
relatively long time [46], and their interest continues until this
very day. Some studies have addressed O-O design patterns to
highlight their importantance in software maintainability,
modularity, stability, re-usability, and quality [15], [16], [19].
These studies show that O-O design patterns have positive
affect over the software quality in general.
One of these studies conducted by Panca, Mardiyanto, and
Hendradjaya [19], implemented a case study of three different
apps categorieslearning, health, and survey. Once without
using O-O design patterns and another with the use of O-O
design patterns. Their results show that the use of design
patterns improves the apps maintainability and modularity.
Another study by Prabhakar et al. [18] studied the effect of
design patterns in data mining systems. A three-layered
architecture component was analyzed to expose this
relationship, and finally prove that using design patterns in the
right circumstances will relatively improve the system
quality.
Another research trend has addressed O-O design patterns
from detection perspective[1], [47]–[54]. Some of these
studies used manual tagging to detect design patterns, others
used machine learning techniques and ontology, while some
other tools used similarity scoring.
Al-Obeidallah, Petridis, and Kapetanakis [20] compared
different O-O design patterns detection approaches, then
show that not all approaches manage to capture all GoF design
patterns. Few studies until this day managed to capture all 23
of GoF design patterns and they are [1], [3], [55], [56].
Oruc, Akal, and Sever [48] create new tool (DesPaD), which
extract design patterns by converting the source code into a
graph model to visually extract them, they compared their
results with related by applying the tool on four different
source codes.
A study by Derezinska and Byczkowski [21] performed some
enhancements on design patterns detection for C#
applications. Their enhancements were applied on the
approach produced by [57], which is also developed for C#
apps design patterns detection. Then they compared the
enhanced version with the original one.
Yu, Zhang, and Chen [3] developed a new approach to detect
all 23 of GoF design patterns. This approach divides the
problem of detecting design patterns into smaller problems,
by defining 15 sub-pattern that combining one, two, or three
of them will formulate one design pattern. These sub-patterns
have been built using four kind of relations between O-O
classes inheritance, association, aggregation, and
dependence, which can be easily captured among the classes.
Furthermore, Yu et al. [58] enhanced their approach by
extending the original one with an improved search order,
which makes it start from the most representative class and
avoid all irrelevant classes, to reduce the search space.
However, as all other studies discussed design patterns
detection in a desktop or web languages like Java, C++, C#,
etc. and none of them handles this dilemma for mobile
languages. The previous study of our research [1] is the only
study that discusses O-O design patterns detection for
Android source code, and has been tested to detect all 23 of
GoF design patterns.
This research will extend PatRoid to be able to work with
several Android app, and then use it to analyze over 1400
Android apps for the usage of O-O design patterns.
3. METHODOLOGY
3.1 Research Questions
This study aims to reveal the extent of usage for O-O design
patterns for Android apps in general, and to study the relation
of using design patterns for specific Android apps categories.
That been said, we have formulated the follow research
question:
RQ1) To what extent are design patterns applied in
Android apps?
The sample apps that have been analyzed were categorized for
31 different categories. In respect to these categories we got
motivated to know if the design patterns found in each
Android app are related to its category or not. Where we can
know if there are categories that are more mature than others,
which leads us to the second research question:
RQ2) Are design patterns applied in certain Android
app categories?
3.2 Data Collection
F-Droid is considered as a free Android apps store. In
addition, it contains links for all the Android apps source code
that it hosts, which makes it a huge repository for a big
representative sample of Android apps, specially to this study
[59].
Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2180
This repository has been used in recent researches such as
[12], [60], [61]. In this research, we collected a sample of1427
Android apps hosted by GitHub and Bitbucket. Later on,
allthese apps were manuallymapped to Google Play to
identify each app category, which resulted in a total of 31
Categories. Table 1 shows the numbers of Android apps
collected in respect to their categories.
Table 1: Android Apps Categories
Category Number of Apps
Tools 337
Games 223
Productivity 147
Personalization 80
Communication 74
Family 70
Music and Audio 54
Entertainment 39
Video Players and Editors 39
Books and Reference 39
Lifestyle 38
Health and Fitness 34
Finance 33
Travel and Local 29
Business 28
Photography 28
Social 27
Maps and Navigation 21
Weather 17
News and Magazines 16
Libraries and Demo 12
Food and Drink 7
Medical 6
Shopping 6
Art and Design 5
Auto and Vehicles 5
Beauty 5
Comics 3
Parenting 2
House and Home 2
Dating 1
Total 1427
3.3 PatRoid
PatRoid is a python open source framework to detect design
patterns in Android app [1]. As an input PatRoid takes the
Android app project directory to be analyzed, or it can work
with the relational model for that specific app.
It starts by gathering AndroidManifest XML file that contains
the app activities information, and all the Java classes based
on each activity.
After categorizing the java classes based on the activities,
PatRoid prepares a relational model from four relations
among the classes, and creates an XML model describes these
relations, Inheritance, Association, Aggregation, and
Dependency.
The relational model yielded is taken as an input to extract
sub-patterns instances. These sub-patterns consist form one or
more relations that can be aggregated later to formulation
design patterns. PatRoid extracts fifteen sub-patterns from the
relational model, Aggregation Parent Inherited, Common
Inheritance, Dependency Child Inheritance, Dependency
Parent Inherited, Indirect Inheritance Aggregation,
Inheritance Aggregation, Inheritance Association, Inheritance
Child Association, Inheritance Child Dependency,
Inheritance Parent Aggregation, Inheritance Parent
Association, Inheritance Parent Dependency, Multi-Level
Inheritance, Self-Aggregation and finally Self-Association.
After all sub-patterns are extracted, PatRoid aggregates each
group of sub-patterns based on a predefined combination to
formulate each design pattern. For example, Singleton design
pattern is detected by finding Self-Association sub-pattern,
while Visitor design pattern is detected by aggregating
Aggregation Parent Inherited, Parent Inherited and
Inheritance Child Dependency sub-patterns, and so on.
Further description over PatRoid structure is illustrated in
Figure 1which shows the different components of PatRoid.
The numbers appear on the figure indicate PatRoid
components states. The first state shows the part where the
tested Android app source code is being parsed. The second
state is a map of relations parsed is returned to create a
relational model in state three and store it in a new XML
model in state four. The relation XML model is given to the
component where extracting sub-patterns is done as shown in
states five and six. Then the component in which these
sub-patterns being aggregated to detect design patterns appear
in states seven and eight. To finally dump the design patterns
detection report.
Figure 1: PatRoid Structure[1]
Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2181
3.4 Extended PatRoid
In this study, PatRoid has been extended by adding the ability
to work with any directory that contains one or more Android
apps source code. As a result, PatRoid will support both
working with one single Android app or with directory full
with Android apps. This change requires adding support for
all kind of Android app projects and exceptions, like single
activity apps, Android apps with missing AndroidMainfest,
Android apps with missing Java files, etc.
Additionally, in order to enhance PatRoid performance to
avoid memory crash specially when analyzing big set of
Android apps as in this study. This study extends PatRoid
output methodology to dump every analyzed app dictionary to
JSON file, which adds the ability to analyze PatRoid results
offline.
Figure 2: Extended PatRoid Workflow
Figure 2 shows the new workflow of PatRoid. The first step is
to take the directory that contains Android apps source code
repositories, to start fetching the apps one by one, and then
creates the relations among classes to build the relational
model. After that it will extract the sub-patterns instances, so
they'll be ready for the aggregation process to detect design
patterns. In the end it will save the design patterns detected
details (like design pattern classes, and combination of
sub-patterns) into a dictionary of all Android apps design
patterns. Then finally check if all Android apps under the root
directory are finished, and save the dictionary into a JSON
file.
4. RESULTS
Results are divided in two parts. The first part shows the
general usage of each design pattern over the whole sample,
and the second part shows design patterns usage distribution
over each apps’ category.
4.1 Design Patterns Usage
Results in this part shows the numbers of Android apps that
uses each design pattern. In addition, the results for not using
any design patterns is shown as well. All these results appear
in Table 2, where all design patterns are sorted based on the
number of Android apps that use it, also it shows the usage
percentage for each design patterns that is the number of
Android apps that use each design pattern from all the
Android apps sample.
Table 2: Design Patterns Usage
Design Pattern Number of
Android Apps
Using it
Usage
Percentage
Singleton 791 55%
Template 593 42%
Adapter 433 30%
Proxy 376 26%
Composite 248 17%
Abstract Factory 187 13%
Chain of
Responsibility 171 12%
Factory 165 12%
Façade 149 10%
Mediator 146 10%
Strategy 144 10%
State 144 10%
Flyweight 143 10%
Prototype 130 9%
Builder 119 8%
Command 119 8%
Memento 107 7%
Observer 79 6%
Bridge 68 5%
Iterator 28 2%
Visitor 21 1%
Interpreter 13 1%
Decorator 4 0%
No Design
Patterns 546 38%
Additionally, a bar chart showing the design patterns usage is
presented in Figure 3, the chart shows the design patterns on
the horizontal axis, and the usage percentage on the vertical
axis.
Figure 3: Design Patterns Usage Chart
Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2182
4.2 Design Patterns Usage Per Category
As discussed before the sample of Android apps is
categorized into 31 categories (as shown in Table 1: Android
Apps Categories). The results of this part show the design
patterns distribution per each category, or in other words,
what kind of design patterns are used in each Android apps
category.
First of all, in Table 3 the number of design patterns applied
by each category is shown, where the number of 23 design
patters is an indication that all of the 23 GoF design patterns
were detected in the Android apps that fall under this
category. Secondly, a more detailed table that shows each
Android apps category with specific list of design patterns
detected in it, is provided in Table 4, where mark indicates
that the Android apps category in that row has at least one
instance of the design pattern in that column.
Table 3: Design Patterns Usage Per Category
Category
Number of Design
Patterns Used
Lifestyle
23
Tools
23
Productivity
22
Communication
22
Books and Reference
22
News and Magazines
21
Entertainment
21
Music and Audio
21
Games
21
Video Pl
ayers and Editors
21
Libraries and Demo
20
Shopping
20
Finance
20
Health and Fitness
20
Business
20
Personalization
20
Photography
19
Social
19
Family
19
Travel and Local
17
Weather
14
Maps and Navigation
13
Art
and Design
13
Beauty
8
Comics
8
Medical
8
Auto and Vehicles
7
Parenting
5
Food and Drink
1
Dating
1
House and Home
1
Moreover, a bar chart that describes each category and the
number of design patterns used in that category is shown in
Figure 4, where the vertical axis shows the design patterns
numbers and the horizontal axis for the Android apps
categories.
Figure 4: Design Patterns Distribution Per Category
5. DISCUSSION
5.1 RQ1 -To what extent are design patterns applied in
Android apps?
Answering the first research question, gives us a lot of
information about the what level of re-usability,
maintainability, and evolution Android apps have, also it
gives us information about the Android apps developers
maturity.
The results shown in Table 2 and illustrated in Figure 3
describe the number of Android apps that use each design
pattern, it is apparent that Singleton design patterns is the top
design pattern used. Over than 55% of Android apps are using
this design pattern, which make sense, because this design
pattern is widely used due to the need of creating a single
activity, other than the usual use of this design pattern.
Secondly, the Template design pattern appears as the second
design pattern with 42% of Android apps are using it, this
because template describes the known IS-A relation, where
two or more classes are inherited from the same super class, or
in other words Common Inheritance sub-pattern.
As for the rest of GoF design patterns, the usage percentage
keep decreasing to reach almost 0% for the Decorator design
pattern. Decorator design pattern provides the code with a
fixable inheritance relationship for objects by changing the
functionalities of objects in run-time. This design pattern
implementation requires a combination of Common
Inheritance and Inheritance Aggregation sub-patterns or
Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2183
Common Inheritance, Inheritance Aggregation and
Multi-Level Inheritance sub-patterns. Both combinations
require strong coding skills to accomplish. The lack of this
kind of design patterns, which is part of other 19 design
patterns that their usage percentage didn't reach 20% of the
total usage among this study sample, in addition to 38% of the
Android apps that don't use any of the 23 GoF design patterns.
These results can lead us to conclude that the Android
development community still not mature enough, also
Android apps in general cannot be consider as highly mature,
maintainable, nor reusable. This conclusion answers our first
research question.
5.2 RQ2 -Are design patterns applied in certain Android
app categories?
Answering this research question gives us a clear comparison
between Android apps categories in term of using design
patterns, which eventually leads us to conclude what category
is more mature than the other.
Each of Table 3 and Table 4 shows a detailed information
about the usage of design patterns per each Android category.
These details are summarized in Figure 4 and from there it can
be observed that there are some categories that use 100% of
the total 23 GoF design patterns (Lifestyle and Tools
Categories), followed by eight categories that use more than
90% of design patterns, then nine categories use more than
abstract factory
adapter
bridge
builder
chain of responsibility
command
composite
decorator
façade
factory
flyweight
interpreter
iterator
mediator
memento
observer
prototype
proxy
singleton
state
strategy
template
visitor
Productivity
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
News and Magazines
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Entertainment
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Libraries and Demo
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Food and Drink
X
Dating
X
Travel and Local
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Shopping
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Finance
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Beauty
X
X
X
X
X
X
X
X
Photography
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Parenting
X
X
X
X
X
Auto and Vehicles
X
X
X
X
X
X
X
Maps and Navigation
X
X
X
X
X
X
X
X
X
X
X
X
X
Music and Audio
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Lifestyle
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Art and Design
X
X
X
X
X
X
X
X
X
X
X
X
X
Comics
X
X
X
X
X
X
X
X
Medical
X
X
X
X
X
X
X
X
Games
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Social
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Health an
d Fitness
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Business
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Family
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Communication
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Video Players and
Editors X X X X X X X X X X X X X X X X X X X X X
Weather
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Personalization
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
House and Home
X
Tools
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Books and Reference
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Table 4: Design Patterns Distribution Per Category
Diaeddin Rimawi et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(2), March - April 2020, 2178 – 2186
2184
80% if design patterns. After that the usage number start to
rapidly decrease with only one category uses more than 70%
if design patterns, one that uses more than 60%, two
categories use more than 50%, four use more than 30%, one
uses more than 20%, and finish with three categories that use
less than 10% of the total 23 GoF design patterns.
Based on these results it appears that there is high diversity
over Android categories maturity, for instance, the Tools
category has the highest amount of Android apps, which
means that it contains vary maturity between the Android
apps belong to this category that leads to fully cumulative
amount of design patterns used in this category apps, and this
conclusion answers the second research question.
6. FUTURE WORK
In extend this study, PatRoid can be enhanced more in the
accuracy part, and in the same context different approaches
can be used with PatRoid, like using machine learning
methods instead of isomorphism approach, and compare the
results between this study and the new PatRoid.
7. THREATS TO VALIDITY
Our sample included more than 1400 Android apps available
over F-Droid. Extending the sample number will indeed
provide better and more accurate results. Further, F-Droid is
the official hub for Android source code, but the number of
apps available is very small compared to the number of apps
available on Google Play.
8. CONCLUSION
As a conclusion, this study shows that the usage of design
patterns in general in Android apps is still needs
improvements specially with only four design patterns has a
usage percentage between 20% to 55%, and the rest nineteen
design patterns are used in a percentage less than 20%, in
addition to 38% of the Android apps that runs in this study are
not using any design patterns.
Additionally, the study shows that there is high diversity over
the different Android apps categories which means that there
are some categories that more mature than others.
This study has proven that the area of Android apps
development is still lack to proper usage of design patterns.
This conclusion means that there is still a lot to be done in this
area, especially with the growth toward using Android in
more critical fields.
Design patterns have proven it importance specially for better
software re-usability, maintainability, and evolution. Which
means that using it is very important to spread the awareness
of its important in Android different fields.
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