8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
Comparison of Code Smells in iOS and Android
Applications
Kristiina Rahkemaa , Dietmar Pfahla
a Institute of Computer Science, University of Tartu, Tartu, Estonia
Abstract
Code smells are patterns indicating bad practices that may lead to maintainability problems. For mobile applications
most of the research has been done on Android applications with very little research on iOS applications. Our goal
is to compare the variety, density, and distribution of code smells in iOS and Android applications. We analysed
273 open source iOS and 694 open source Android applications. We used PAPRIKA and GraphifySwift to find 19
object oriented code smells. We discovered that the distributions and proportions of code smells in iOS and Android
applications differ. More specifically, we found: a) with the exception of one code smell (DistortedHierarchy) all
code smells that could be observed in Android apps also occurred in iOS apps; b) the overall density of code smells is
higher on iOS than on Android with LazyClass and DataClass particularly sticking out; c) with regards to frequency,
code smells are more evenly distributed on iOS than on Android, and the distributions of code smell occurrences on
class level are more different between the platforms than on app level.
Keywords
Mobile applications, Android, iOS, Code smells
1. Introduction proportions of code smells on these platforms.
They analysed iOS apps for four object oriented,
Code smells are patterns indicating bad practices three iOS specific and Android apps for four ob-
that often lead to maintainability problems [1]. ject oriented and two Android specific code smells.
Code smells have been studied extensively for They discovered that code smell proportions were
desktop applications (shortened to ”apps” in the higher in Android apps.
following). For mobile apps most of the analysis Our goal is to compare the variety, density and
has been done on the Android platform. distribution of code smells in iOS and Android
Mannan et al. [2] analyzed 21 object oriented apps. First we will check if variety, density and dis-
code smells in open source Android apps. They tribution of code smells differ in iOS and Android
compared code smell occurrences on Android and apps to see if the results are similar to differences
Java desktop apps looking at differences in variety, found between Android and desktop Java apps by
density and distribution of code smells. They dis- Mannan et al. [2]. Second we extend the analysis
covered that the variety of code smells is the same, done by Habchi et al. [3] by comparing the den-
but density and distribution of code smells in desk- sities and distributions of more code smells in iOS
top Java and Android apps differ. They mention and Android apps, to see if Android apps are in
that other mobile platforms should have the same general more prone to code smells and if different
variety of code smells but do not discuss possible platforms are more prone to different code smells.
differences in density or distribution. In this study we aim to answer the following re-
Habchi et al. [3] used the tool PAPRIKA [4] search questions:
to analyse iOS and Android apps and compared RQ 1: Are all types of object-oriented code
smells present in both iOS and Android apps?
QuASoQ 2020: 8th International Workshop on Quantitative To answer this and the following research ques-
Approaches to Software Quality, December 1st, 2020, Singapore tions, we used the tool GraphifySwift1 [5] to anal-
" kristiina.rahkema@ut.ee (K. Rahkema); yse iOS Apps and the tool PAPRIKA [4] to anal-
dietmar.pfahl@ut.ee (D. Pfahl) yse Android apps. To make the code smell defi-
0000-0003-2400-501X (D. Pfahl)
© 2020 Copyright for this paper by its authors. Use permitted under nitions (and calculations) comparable across plat-
Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
http://ceur-ws.org
CEUR Workshop Proceedings (CEUR- 1 https://github.com/kristiinara/GraphifySwift
WS.org)
Workshop ISSN 1613-0073
Proceedings
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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
forms, we adapted the code smell queries defined ties: the proportions of code smells differ between
by Rahkema et al. [5] when searching for code platforms. In addition, we saw that code smells are
smells in Android apps. In total, we identified 19 more evenly distributed in iOS apps as compared
code smell types that could potentially occur in to Android apps.
apps on both platforms. We took under consider- Then we analyzed how large the share of smelly
ation that the variety of code smells depends on apps on each platform is and how large the share
the programming language used. For example, the of smelly classes is on each platform. We did these
code smell RefusedParentBequest is not applica- analyses for each code smell type separately. It
ble to Swift because Swift lacks the 𝑝𝑟𝑜𝑡𝑒𝑐𝑡𝑒𝑑 key- turned out that the percentages of smelly apps
word. Therefore, we did not include it in our anal- are relatively similar between platforms. Only the
yses. code smell DataClass is much more prominent in
Our analysis showed that 18 of the 19 identified iOS apps than in Android apps.
code smells occurred in apps on both platforms, In addition, we found that the distributions of
i.e., Android and iOS. Code smell DistortedHier- code smell occurrences on class level are more dif-
archy never occurred in iOS apps. ferent between the platforms than on app level.
To better understand whether the frequency of This result might, again, be explained by the fact
occurrence is similar, we formulated our second re- that Android apps usually have larger classes and,
search question. thus, tend to have more of the code smells that cor-
RQ 2: Do code smells occur with the same respond to more complex classes whereas iOS apps
density in iOS and Android apps? tend to have more compact classes and, thus, tend
To answer this question, we calculated the over- to have more of the code smells that correspond to
all density of all code smells and the densities of more simple classes. This effect is more prominent
each of the 19 code smells over all apps on both iOS when doing the analysis on class level than on app
and Android. It turned out that, contrary to what level.
Habchi et al. [3] expected, the overall density of
code smells is higher in iOS apps than in Android
apps. Code smells LazyClass, DivergentChange, 2. Related Work
PrimitiveObsession and DataClass had a particu-
Code smells in desktop applications: Fowler
larly high density in iOS apps. On the other hand,
[1] defined 22 object oriented code smells and pro-
code smells LongMethod, LongParameterList and
vided refactorings for these code smells. Khomh
ShotgunSurgery were clearly more frequent in An-
et al. [6] studied the impact of code smells. They
droid apps. In addition, we found that the code
found that code smells affect classes negatively
smell densities per code smell type were some-
and that classes with more code smells were more
times higher and sometimes smaller in iOS apps
prone to changes [6]. Olbrich et al. [7] studied the
as compared to Android apps. This might be ex-
evolution and impact of code smells based on two
plained by the fact that Android apps tend to have
open source systems. Their findings confirmed
more of the code smells that correspond to more
that code smells affect the way how code changes
complex classes whereas iOS apps tend to have
in a negative way. They were also able to iden-
more of the code smells that correspond to more
tify different phases of evolution in code smells [7].
simple classes.
Linares et al. [8] made a large scale analysis of
To better understand the distributions of code
Java Mobile apps and discovered that anti-patterns
smells in apps on the two platforms iOS and An-
negatively impact software quality metrics such as
droid, we formulated our third research question.
fault-proneness [8].
RQ 3: Do code smell distributions differ be-
Tufano et al. [9] studied the change history
tween iOS and Android apps?
of 200 open source projects and found that most
To answer this question we first compared the
code smells are introduced when the correspond-
proportions of code smell occurrences across all
ing code is created and not when it is changed.
iOS and Android apps. The results confirmed what
They also found that when code does become
we had seen when we compared code smell densi-
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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
smelly through evolution then it can be character- code smells studied was limited due to the num-
ized by specific code metrics. Contrary to common ber of code smells PAPRIKA is able to detect and
belief [10] they discovered that most code smells ranged from three to four object oriented code
are not introduced by newcomers, but by develop- smells and four to six Android specific code smells
ers with high work loads and high release pressure [15][13][14]. Mannan et al. [2] decided to broaden
[9]. this scope and studied 21 object oriented code
Code smells in Android applications: Dif- smells using the commercial tool InFusion. They
ferent kinds of code smells have been researched analyzed open source Android and Java desktop
for Android, such as object-oriented, Android- apps for these 21 code smells and compared their
specific, security-related and energy-related code occurrences. Mannan et al. detected that the va-
smells. Gottschalk et al. proposed an approach riety of code smells was the same and most code
to detect energy related code smells on mobile smells occur in both systems in a similar frequency
apps and validated this approach on Android and with major differences only for a couple of code
showed that it is possible to reduce energy con- smells. They concluded that studying code smells
sumption by refactoring the code [11]. Ghafari et on mobile platforms can be done with tools meant
al. [12] studied security-related code smells and for desktop apps. They also found that the code
discovered that most apps contain at least some smells that have been researched so far are not
security-related code smells. the same ones that occur most and that the focus
Hecht [4] proposed an approach to detect code should change to code smells that are more rele-
smells and anti-patterns on Android systems and vant [2]. They suggest that other mobile platforms
implemented this approach in a tool called PA- will have the same code smells, but do not give
PRIKA. This tool analyses the Android APK, cre- any suggestions towards the possible differences
ates a model of the code and inserts this model into in density or distribution. They analysed 500 An-
the neo4j database. Code smells are then defined as droid and 750 Java desktop apps randomly selected
database queries which makes it possible to query from GitHub. Unfortunately, the tool Infusion used
code smells on a large number of apps at the same by Mannan et al. does not seem to be available
time. He analysed 15 popular apps for the occur- anymore. Therefore a direct comparison using In-
rences of four object oriented and three Android fusion for code smell analysis on iOS is no longer
code smells. Hecht et al. [13] tracked, the soft- possible.
ware quality of 106 popular Android apps down- Code smells in iOS applications: Habchi et
loaded from the Google Play Store along their evo- al. [3] used PAPRIKA to detect code smells in
lution. They calculated software quality scores for iOS apps. They used ANTLR4 grammars to gen-
different versions of these apps and tracked their erate parsers for Swift and Objective-C code. They
evolution. There were different evolution graphs, created the apps graphs that could then be used
such as constant decline, constant rise, stability or by PAPRIKA. They analysed 176 Swift and 103
sudden change in either direction depending on Objective-C apps from a collaborative list of open
the programming practices of the team [13]. This source iOS apps. In their study they analysed four
shows that code quality is not necessary linked object oriented, three iOS specific and two Android
to app size, but the programming practices of the specific code smells. They compared smell propor-
developers. Mateus et al. [14] used PAPRIKA to tions in iOS and Android apps and discovered that
analyze Android apps written in Java and kotlin. the proportions of code smells were higher in An-
They compared code smell occurrences in both droid apps. On the other hand proportions of code
languages and concluded that apps that were ini- smells in Objective-C and Swift were similar [3].
tially written in Java and later introduced kotlin Rahkema et al. [5] introduced a tool called
were of better quality than other Android apps GraphifySwift that analyses Swift code and detects
[14]. They analysed a set of 2167 open source An- 34 object oriented code smells. Similarly to PA-
droid apps combining different databases of open PRIKA, GraphifySwift enters data about the anal-
source Android apps. ysed app into the neo4j database. The database
In these papers using PAPRIKA the number of structures used by PAPRIKA and GraphifySwift
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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
are similar, but slightly different. In their analy- For Android apps we used PAPRIKA to populate
sis they used the same collaborative list of open the neo4j database. We then took the queries de-
source iOS apps but did not compare the results to fined by Rahkema et al. for GraphifySwift to find
Android. code smells. Since GraphifySwift was originally
In the following, we extend the research in developed to analyse iOS apps we had to adapt the
[2, 3, 5]. We adapted the queries defined in [5], code smell queries so that they could be used on
where possible, so that they could be applied to the database produced by PAPRIKA. We made the
a database populated by PAPRIKA. We used PA- following changes to the code smell queries:
PRIKA to analyse Android apps and GraphifySwift We removed references to Module nodes, i.e.,
to analyse Swift apps. Then we compared the two the relationship
platforms with regards to variety, density, and dis-
(app)-APP_OWNS_MODULE->(module)-
tribution of 19 code smells.
MODULE_OWNS_CLASS->(class)
was substituted by the relationship
3. Methods
(app)-APP_OWNS_CLASS->(class)
In Section 3.1, we present the tools used for code
smell analysis. In Section 3.2, we cover the choice We removed references to argument type or
of apps and in Section 3.3 we describe the analysis substituted them with argument name. Argu-
performed. ment names are not accessible in Java bytecode
and therefore the argument name provided by PA-
PRIKA is actually the argument type.
3.1. Code Smell Analysis Finally, we added the relationship
In previous research a tool called PAPRIKA has
(variable|argument)-IS_OF_TYPE
been used to find code smells in Android appli-
->(class)
cations [4, 15, 13, 3, 14]. PAPRIKA analyses the
Android APK, enters data about the applications by finding classes whose name matched the argu-
into a neo4j database and defines queries for each ment name or variable type.
code smell. For analysing iOS applications Habchi After these modifications of the database and
et al. [3] used PAPRIKA to query code smells, but queries, 19 of the 34 GraphifySwift code smell
populated the neo4j database using ANTLR gram- queries could be used on the Android app database
mars. Rahkema et al. [5] introduced a new tool produced by PAPRIKA.
called GraphifySwift that extends the functional- The code smell queries that had to be excluded
ity of PAPRIKA. It analyses Swift code, enters data contained metrics or attributes that were not pro-
about the iOS applications into a neo4j database vided by PAPRIKA. We excluded for example
and defines database queries to find code smells. queries referring to code duplication, maximum
PAPRIKA is able to find four object oriented code nesting depth, number of switch statements and
smells. Since the queries for these four code smells number of comments.
are implemented identically in GraphifySwift, it For the analysis of Android apps we calculated
produces the same results as PAPRIKA for them. new thresholds based on the apps that we anal-
In GraphifySwift additional code smell queries are ysed. The list of iOS and Android thresholds is in-
defined. Overall, GraphySwift is able to find 34 ob- cluded in the thresholds table2 .
ject oriented code smells.
For the analysis of iOS apps we used the tool
3.2. Choice of Applications
GraphifySwift. We used the same thresholds as
in Rahkema et al. [5]. Note that we focused on For analysis of iOS apps we used the same collab-
Swift code as Swift has replaced Objective-C and orative list of open source iOS apps as was used by
not many differences between the two languages 2 https://figshare.com/articles/conference_contribution/
are to be expected according to Habtchi et al. [3]. Thresholds_for_iOS_and_Android_code_smell_analysis/
13102991
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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
Rahkema et al. [5] and whose older version was DistortedHierarchy, DivergentChange, Inappro-
used by Habchi et al. [3]. The final set of success- priateIntimacy, LazyClass, LongMethod, LongPa-
fully analysed apps was the same as in [5] and in- rameterList, MiddleMan, ParallelInheritanceHier-
cluded 273 open source iOS apps. archies, PrimitiveObsession, SAPBreaker, Shot-
For analysis of Android apps we took the list of gunSurgery, SpeculativeGeneralityProtocol, Swis-
apps provided by Habchi et al. [3]. Since the list sArmyKnife and TraditionBreaker.
only included app package names, we queried All- Below, we present and discuss the results for
FreeAPK api3 to find and download these apps. We each research question separately.
decided to search AllFreeAPK instead of GitHub, RQ 1: Are all types of object-oriented code
as PAPRIKA uses APKs for analysis and this way smells present in both iOS and Android apps?
we were able to skip the step of compiling these When comparing the occurrence of code smells
apps. Later during the analysis we needed to dis- on each platform, we found that 18 of the 19 iden-
card some of the very big apps due to performance tified code smells occurred in apps on both plat-
issues. In total we included 694 open source An- forms, i.e., Android and iOS. Code smell Distort-
droid apps in our analysis. edHierarchy never occurred in iOS apps.
Our result does not fully support Mannan et al.’s
3.3. Data Analysis expectation that mobile apps on other platforms
than Android should exhibit the same code smells
To answer RQ1, we checked whether any of the 19 [2].
identified code smells occurred in at least one app RQ 2: Do code smells occur with the same
on each platform. density in iOS and Android apps?
To answer RQ2, we calculated the densities of The results of our code smell density analysis
code smells for both iOS and Android apps and is shown in Figure 1. Accumulated over all code
compared these. Code smell density was calcu- smells it turned out that the apps on the iOS plat-
lated by counting the number of code smells (total form had a density of 41.7 smells/kilo-instructions
and per code smell type) and dividing by the num- while the apps on Android only had a density of
ber app instructions. 34.4 smells/kilo-instructions. This result is con-
To answer RQ3, we had to perform several cal- trary to what Habchi et al. [3] expected.
culations. To calculate the relative frequencies of Moreover, it can be seen from Figure 1 that the
code smells per code smell type on each platform, code smell densities differ between iOS and An-
we counted the code smells of a type in all apps droid. Code smells LazyClass, DivergentChange,
and divided by the total code smell count. We did PrimitiveObsession and DataClass had a particu-
this per platform. To calculate the code smell dis- larly high density in iOS apps. On the other hand,
tributions on app and class levels per platform, we code smells LongMethod, LongParameterList and
counted how many apps (and classes) contain at ShotgunSurgery were clearly more frequent in An-
least one code smell of a certain type and then di- droid apps. The fact that code smell densities
vided by the total number of apps (and classes). were sometimes higher and sometimes lower in
iOS apps as compared to Android apps might be
explained by the fact that Android apps tend to
4. Results have more of the code smells that correspond to
more complex classes whereas iOS apps tend to
We analysed 273 open source iOS apps using
have more of the code smells that correspond to
GraphifySwift and 694 open source Android apps
more simple classes.
using PAPRIKA and modified code smell queries
RQ 3: Do code smell distributions differ be-
from GraphifySwift to answer our research ques-
tween iOS and Android apps?
tions. We analyzed the apps with regards to
Figure 2 shows the relative frequency of code
19 code smells: BlobClass, ComplexClass, Cyclic-
smell occurrences over all apps on the Android
ClassDependency, DataClass, DataClumpFields,
platform (blue bars) and the iOS platform (red
3 https://m.allfreeapk.com/api/ bars). The results confirm what we had seen when
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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
Code smell count per 1000 instructions
8 Android 80 Android
iOS iOS
% of apps with code smell
6 60
4 40
2 20
0 0
LongMethod
LongParameterList
ShotgunSurgery
LazyClass
SAPBreaker
DivergentChange
PrimitiveObsession
DataClumpFields
ComplexClass
InappropriateIntimacy
DistortedHierarchy
BlobClass
CyclicClassDependency
SpeculativeGeneralityProtocol
DataClass
SwissArmyKnife
ParallelInheritanceHierarchies
TraditionBreaker
MiddleMan
LazyClass
LongMethod
LongParameterList
ShotgunSurgery
SAPBreaker
DivergentChange
PrimitiveObsession
ComplexClass
DataClumpFields
BlobClass
InappropriateIntimacy
DistortedHierarchy
CyclicClassDependency
SpeculativeGeneralityProtocol
DataClass
SwissArmyKnife
ParallelInheritanceHierarchies
TraditionBreaker
MiddleMan
Figure 1: Comparison of code smell densities between Figure 3: Comparison of code smell frequencies on app
Android (blue) and iOS (red) apps level between Android (blue) and iOS (red)
25
while only 7% of Android apps are affected), Mid-
Android
dleMan (15% of iOS apps are affected but only 1%
of Android apps), and DistortedHierarchy (25 % of
20 iOS
% of code smells
15
Android apps are affected but none of the iOS apps
10 is).
5
We found that the distributions of code smell oc-
currences on class level are more different between
0
the platforms than on app level. This result might,
LongMethod
LongParameterList
ShotgunSurgery
LazyClass
SAPBreaker
DivergentChange
PrimitiveObsession
DataClumpFields
ComplexClass
InappropriateIntimacy
DistortedHierarchy
BlobClass
CyclicClassDependency
SpeculativeGeneralityProtocol
DataClass
SwissArmyKnife
ParallelInheritanceHierarchies
TraditionBreaker
MiddleMan
again, be explained by the fact that Android apps
usually have larger classes and, thus, tend to have
more of the code smells that correspond to more
complex classes whereas iOS apps tend to have
Figure 2: Code smell proportions on Android (blue) and
more compact classes and, thus, tend to have more
iOS (red) of the code smells that correspond to more simple
classes. This effect is more prominent when doing
the analysis on class level than on app level.
In addition, we analyzed the occurrence of the
we compared code smell densities: the proportions
method-based code smells LongMethod and Long-
of code smells differ between platforms. In addi-
ParameterList separately. We found that in iOS
tion, we see that code smells are more evenly dis-
apps 9% of methods are considered LongMethod
tributed in iOS apps as compared to Android apps.
while this is the case for 14% of the methods in An-
Then we analyzed how large the share of smelly
droid apps. In iOS apps 5% of the methods have a
apps on each platform is and how large the share
LongParameterList while this is the case for 9% of
of smelly classes is on each platform. We did these
methods in Android apps.
analyses for each code smell type separately.
Figures 3 and 4 show the percentages of apps
and classes, respectively, containing code smells of 5. Threats to Validity
a certain type.
We found that the percentages of smelly apps Internal Validity: In our case internal validity
are relatively similar between platforms. The might be affected by how code smells are detected
biggest differences occurs for code smell DataClass by the tools used. PAPRIKA has been used in mul-
(79% of iOS apps have at least one affected class tiple studies [15, 13, 3, 14]. GraphifySwift was in-
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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
GraphifySwift is open source and also available on
40
the tool GitHub page. For Android analysis we
used the list of apps analysed by [3], the list of suc-
Android
% of classes with code smell
iOS
30
cessfully analysed apps can be found in the list of
20
apps4 . PAPRIKA is open source and also available
on GitHub. The adapted code smell queries used
10
for Android analysis can be found in the list of An-
0
droid code smell queries5 .
LazyClass
LongMethod
LongParameterList
ShotgunSurgery
SAPBreaker
DivergentChange
DataClumpFields
ComplexClass
InappropriateIntimacy
PrimitiveObsession
DistortedHierarchy
BlobClass
DataClass
CyclicClassDependency
SpeculativeGeneralityProtocol
ParallelInheritanceHierarchies
SwissArmyKnife
TraditionBreaker
MiddleMan
6. Conclusion
Mannan et al. [2] analysed the density and dis-
tribution of code smells in Android apps. We cal-
Figure 4: Comparison of code smell frequencies on
class level between Android (blue) and iOS (red)
culated a similar density and distribution for iOS
and Android apps and saw that these densities and
distributions were different. Additionally we dis-
covered that one of the code smells analysed by
troduced in [5] and validated by replicating results
Mannan et al. was not present in iOS apps.
in [3]. We adapted code smell queries defined in
Habchi et al. [3] compared ratios of code smell
[5], but did so by not changing the code smell def-
occurrences on iOS and Android. We extended
initions themselves.
their research by adding additional code smells
External Validity: We analysed open source
to the analysis and found that code smell occur-
apps. For swift the analysis can only be performed
rences are not always higher in Android apps. For
if the code of the app is accessible. For Android the
some code smells they were higher in iOS apps.
analysis could be performed on apps from the app
This shows that Android apps are not necessar-
store. Therefore for both platforms open source
ily smellier, but different kinds of code smells are
apps were chosen. Previously [5] it was shown that
more prevalent depending on the platform.
although there are some differences between apps
These results can be interesting for developers
that are on the app store the differences are small.
moving from one platform to the other. It can also
On the iOS platform we only analyzed apps written
be useful for developers of tools for these plat-
in Swift. Given that Objective-C and Swift code is
forms. We see that the emphasis on which code
quite similar, we assume our results extend to apps
smells to look at is different depending on the plat-
written in Objective-C.
form.
Construct Validity: GraphifySwift uses stan-
dard definitions of code smells found in literature
[5]. In code smell queries we use thresholds calcu- Acknowledgments
lated based on the app set analysed. Using thresh-
olds is a common approach for detecting code This research was partly funded by the Estonian
smells. We used the same method to determine Center of Excellence in ICT research (EXCITE),
thresholds as was used by Hecht et al. [13], Habchi the IT Academy Programme for ICT Research De-
et al. [3] and [5]. Thresholds might differ between velopment, the Austrian ministries BMVIT and
languages, but since they are calculated based on BMDW, and the Province of Upper Austria un-
the current set of apps analysed language specific der the COMET (Competence Centers for Excel-
differences should be resolved. lent Technologies) Programme managed by FFG,
Reliability: For iOS analysis we used the same
collaborative list of open source iOS apps written 4 https://figshare.com/articles/dataset/iOS_and_Android_
in Swift as was used in [5]. All these apps are app_analysis_data/13103012
5 https://figshare.com/articles/conference_contribution/
available on GitHub. The list of successfully anal-
GraphifySwift_queries_adapted_for_PAPARIKA_for_
ysed apps can be found on the tool GitHub page. Android_code_smell_analysis/13102994
85
8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)
and by the group grant PRG887 of the Estonian Re- ACM, 2014, pp. 232–243.
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thorough review of a previous version of this pa- M. Di Penta, A. De Lucia, D. Poshyvanyk,
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in: Proc. of the 37th Int’l Conf. on Software
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