Comparison of Code Smells in iOS and AndroidApplicationsKristiina Rahkemaa, Dietmar Pfahla

aInstitute of Computer Science, University of Tartu, Tartu, Estonia

AbstractCode smells are patterns indicating bad practices that may lead to maintainability problems. For mobile applicationsmost of the research has been done on Android applications with very little research on iOS applications. Our goalis to compare the variety, density, and distribution of code smells in iOS and Android applications. We analysed273 open source iOS and 694 open source Android applications. We used PAPRIKA and GraphifySwift to find 19object oriented code smells. We discovered that the distributions and proportions of code smells in iOS and Androidapplications differ. More specifically, we found: a) with the exception of one code smell (DistortedHierarchy) allcode smells that could be observed in Android apps also occurred in iOS apps; b) the overall density of code smells ishigher 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 onclass level are more different between the platforms than on app level.

KeywordsMobile applications, Android, iOS, Code smells

1. IntroductionCode smells are patterns indicating bad practicesthat often lead to maintainability problems [1].Code smells have been studied extensively fordesktop applications (shortened to ”apps” in thefollowing). For mobile apps most of the analysishas been done on the Android platform.

Mannan et al. [2] analyzed 21 object orientedcode smells in open source Android apps. Theycompared code smell occurrences on Android andJava desktop apps looking at differences in variety,density and distribution of code smells. They dis-covered that the variety of code smells is the same,but density and distribution of code smells in desk-top Java and Android apps differ. They mentionthat other mobile platforms should have the samevariety of code smells but do not discuss possibledifferences in density or distribution.

Habchi et al. [3] used the tool PAPRIKA [4]to analyse iOS and Android apps and compared

QuASoQ 2020: 8th International Workshop on QuantitativeApproaches to Software Quality, December 1st, 2020, Singapore" kristiina.rahkema@ut.ee (K. Rahkema);dietmar.pfahl@ut.ee (D. Pfahl)� 0000-0003-2400-501X (D. Pfahl)

© 2020 Copyright for this paper by its authors. Use permitted underCreative Commons License Attribution 4.0 International (CC BY 4.0).

CEURWorkshopProceedings

http://ceur-ws.orgISSN 1613-0073

CEUR Workshop Proceedings (CEUR-WS.org)

proportions of code smells on these platforms.They analysed iOS apps for four object oriented,three iOS specific and Android apps for four ob-ject oriented and two Android specific code smells.They discovered that code smell proportions werehigher in Android apps.

Our goal is to compare the variety, density anddistribution of code smells in iOS and Androidapps. First we will check if variety, density and dis-tribution of code smells differ in iOS and Androidapps to see if the results are similar to differencesfound between Android and desktop Java apps byMannan et al. [2]. Second we extend the analysisdone by Habchi et al. [3] by comparing the den-sities and distributions of more code smells in iOSand Android apps, to see if Android apps are ingeneral more prone to code smells and if differentplatforms are more prone to different code smells.In this study we aim to answer the following re-search questions:

RQ 1: Are all types of object-oriented codesmells present in both iOS and Android apps?

To answer this and the following research ques-tions, we used the tool GraphifySwift1 [5] to anal-yse iOS Apps and the tool PAPRIKA [4] to anal-yse Android apps. To make the code smell defi-nitions (and calculations) comparable across plat-

1https://github.com/kristiinara/GraphifySwift

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

79

mailto:kristiina.rahkema@ut.eemailto:dietmar.pfahl@ut.eehttps://orcid.org/0000-0003-2400-501Xhttps://creativecommons.org/licenses/by/4.0http://ceur-ws.orghttp://ceur-ws.orghttp://ceur-ws.orghttps://github.com/kristiinara/GraphifySwift

forms, we adapted the code smell queries definedby Rahkema et al. [5] when searching for codesmells in Android apps. In total, we identified 19code smell types that could potentially occur inapps on both platforms. We took under consider-ation that the variety of code smells depends onthe programming language used. For example, thecode smell RefusedParentBequest is not applica-ble to Swift because Swift lacks the 𝑝𝑟𝑜𝑡𝑒𝑐𝑡𝑒𝑑 key-word. Therefore, we did not include it in our anal-yses.

Our analysis showed that 18 of the 19 identifiedcode smells occurred in apps on both platforms,i.e., Android and iOS. Code smell DistortedHier-archy never occurred in iOS apps.

To better understand whether the frequency ofoccurrence is similar, we formulated our second re-search question.

RQ 2: Do code smells occur with the samedensity in iOS and Android apps?

To answer this question, we calculated the over-all density of all code smells and the densities ofeach of the 19 code smells over all apps on both iOSand Android. It turned out that, contrary to whatHabchi et al. [3] expected, the overall density ofcode smells is higher in iOS apps than in Androidapps. Code smells LazyClass, DivergentChange,PrimitiveObsession and DataClass had a particu-larly high density in iOS apps. On the other hand,code smells LongMethod, LongParameterList andShotgunSurgery were clearly more frequent in An-droid apps. In addition, we found that the codesmell densities per code smell type were some-times higher and sometimes smaller in iOS appsas compared to Android apps. This might be ex-plained by the fact that Android apps tend to havemore of the code smells that correspond to morecomplex classes whereas iOS apps tend to havemore of the code smells that correspond to moresimple classes.

To better understand the distributions of codesmells in apps on the two platforms iOS and An-droid, we formulated our third research question.

RQ 3: Do code smell distributions differ be-tween iOS and Android apps?

To answer this question we first compared theproportions of code smell occurrences across alliOS and Android apps. The results confirmed whatwe had seen when we compared code smell densi-

ties: the proportions of code smells differ betweenplatforms. In addition, we saw that code smells aremore evenly distributed in iOS apps as comparedto Android apps.

Then we analyzed how large the share of smellyapps on each platform is and how large the shareof smelly classes is on each platform. We did theseanalyses for each code smell type separately. Itturned out that the percentages of smelly appsare relatively similar between platforms. Only thecode smell DataClass is much more prominent iniOS apps than in Android apps.

In addition, we found that the distributions ofcode smell occurrences on class level are more dif-ferent between the platforms than on app level.This result might, again, be explained by the factthat Android apps usually have larger classes and,thus, tend to have more of the code smells that cor-respond to more complex classes whereas iOS appstend to have more compact classes and, thus, tendto have more of the code smells that correspond tomore simple classes. This effect is more prominentwhen doing the analysis on class level than on applevel.

2. Related WorkCode smells in desktop applications: Fowler[1] defined 22 object oriented code smells and pro-vided refactorings for these code smells. Khomhet al. [6] studied the impact of code smells. Theyfound that code smells affect classes negativelyand that classes with more code smells were moreprone to changes [6]. Olbrich et al. [7] studied theevolution and impact of code smells based on twoopen source systems. Their findings confirmedthat code smells affect the way how code changesin a negative way. They were also able to iden-tify different phases of evolution in code smells [7].Linares et al. [8] made a large scale analysis ofJava Mobile apps and discovered that anti-patternsnegatively impact software quality metrics such asfault-proneness [8].

Tufano et al. [9] studied the change historyof 200 open source projects and found that mostcode smells are introduced when the correspond-ing code is created and not when it is changed.They also found that when code does become

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

80

smelly through evolution then it can be character-ized by specific code metrics. Contrary to commonbelief [10] they discovered that most code smellsare not introduced by newcomers, but by develop-ers with high work loads and high release pressure[9].

Code smells in Android applications: Dif-ferent kinds of code smells have been researchedfor Android, such as object-oriented, Android-specific, security-related and energy-related codesmells. Gottschalk et al. proposed an approachto detect energy related code smells on mobileapps and validated this approach on Android andshowed that it is possible to reduce energy con-sumption by refactoring the code [11]. Ghafari etal. [12] studied security-related code smells anddiscovered that most apps contain at least somesecurity-related code smells.

Hecht [4] proposed an approach to detect codesmells and anti-patterns on Android systems andimplemented this approach in a tool called PA-PRIKA. This tool analyses the Android APK, cre-ates a model of the code and inserts this model intothe neo4j database. Code smells are then defined asdatabase queries which makes it possible to querycode smells on a large number of apps at the sametime. He analysed 15 popular apps for the occur-rences of four object oriented and three Androidcode smells. Hecht et al. [13] tracked, the soft-ware quality of 106 popular Android apps down-loaded from the Google Play Store along their evo-lution. They calculated software quality scores fordifferent versions of these apps and tracked theirevolution. There were different evolution graphs,such as constant decline, constant rise, stability orsudden change in either direction depending onthe programming practices of the team [13]. Thisshows that code quality is not necessary linkedto app size, but the programming practices of thedevelopers. Mateus et al. [14] used PAPRIKA toanalyze Android apps written in Java and kotlin.They compared code smell occurrences in bothlanguages and concluded that apps that were ini-tially written in Java and later introduced kotlinwere of better quality than other Android apps[14]. They analysed a set of 2167 open source An-droid apps combining different databases of opensource Android apps.

In these papers using PAPRIKA the number of

code smells studied was limited due to the num-ber of code smells PAPRIKA is able to detect andranged from three to four object oriented codesmells and four to six Android specific code smells[15][13][14]. Mannan et al. [2] decided to broadenthis scope and studied 21 object oriented codesmells using the commercial tool InFusion. Theyanalyzed open source Android and Java desktopapps for these 21 code smells and compared theiroccurrences. Mannan et al. detected that the va-riety of code smells was the same and most codesmells occur in both systems in a similar frequencywith major differences only for a couple of codesmells. They concluded that studying code smellson mobile platforms can be done with tools meantfor desktop apps. They also found that the codesmells that have been researched so far are notthe same ones that occur most and that the focusshould change to code smells that are more rele-vant [2]. They suggest that other mobile platformswill have the same code smells, but do not giveany suggestions towards the possible differencesin density or distribution. They analysed 500 An-droid and 750 Java desktop apps randomly selectedfrom GitHub. Unfortunately, the tool Infusion usedby Mannan et al. does not seem to be availableanymore. Therefore a direct comparison using In-fusion for code smell analysis on iOS is no longerpossible.

Code smells in iOS applications: Habchi etal. [3] used PAPRIKA to detect code smells iniOS apps. They used ANTLR4 grammars to gen-erate parsers for Swift and Objective-C code. Theycreated the apps graphs that could then be usedby PAPRIKA. They analysed 176 Swift and 103Objective-C apps from a collaborative list of opensource iOS apps. In their study they analysed fourobject oriented, three iOS specific and two Androidspecific code smells. They compared smell propor-tions in iOS and Android apps and discovered thatthe proportions of code smells were higher in An-droid apps. On the other hand proportions of codesmells in Objective-C and Swift were similar [3].

Rahkema et al. [5] introduced a tool calledGraphifySwift that analyses Swift code and detects34 object oriented code smells. Similarly to PA-PRIKA, GraphifySwift enters data about the anal-ysed app into the neo4j database. The databasestructures used by PAPRIKA and GraphifySwift

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

81

are similar, but slightly different. In their analy-sis they used the same collaborative list of opensource iOS apps but did not compare the results toAndroid.

In the following, we extend the research in[2, 3, 5]. We adapted the queries defined in [5],where possible, so that they could be applied toa database populated by PAPRIKA. We used PA-PRIKA to analyse Android apps and GraphifySwiftto analyse Swift apps. Then we compared the twoplatforms with regards to variety, density, and dis-tribution of 19 code smells.

3. MethodsIn Section 3.1, we present the tools used for codesmell analysis. In Section 3.2, we cover the choiceof apps and in Section 3.3 we describe the analysisperformed.

3.1. Code Smell AnalysisIn previous research a tool called PAPRIKA hasbeen used to find code smells in Android appli-cations [4, 15, 13, 3, 14]. PAPRIKA analyses theAndroid APK, enters data about the applicationsinto a neo4j database and defines queries for eachcode smell. For analysing iOS applications Habchiet al. [3] used PAPRIKA to query code smells, butpopulated the neo4j database using ANTLR gram-mars. Rahkema et al. [5] introduced a new toolcalled GraphifySwift that extends the functional-ity of PAPRIKA. It analyses Swift code, enters dataabout the iOS applications into a neo4j databaseand defines database queries to find code smells.PAPRIKA is able to find four object oriented codesmells. Since the queries for these four code smellsare implemented identically in GraphifySwift, itproduces the same results as PAPRIKA for them.In GraphifySwift additional code smell queries aredefined. Overall, GraphySwift is able to find 34 ob-ject oriented code smells.

For the analysis of iOS apps we used the toolGraphifySwift. We used the same thresholds asin Rahkema et al. [5]. Note that we focused onSwift code as Swift has replaced Objective-C andnot many differences between the two languagesare to be expected according to Habtchi et al. [3].

For Android apps we used PAPRIKA to populatethe neo4j database. We then took the queries de-fined by Rahkema et al. for GraphifySwift to findcode smells. Since GraphifySwift was originallydeveloped to analyse iOS apps we had to adapt thecode smell queries so that they could be used onthe database produced by PAPRIKA. We made thefollowing changes to the code smell queries:

We removed references to Module nodes, i.e.,the relationship

(app)-APP_OWNS_MODULE->(module)-MODULE_OWNS_CLASS->(class)

was substituted by the relationship

(app)-APP_OWNS_CLASS->(class)

We removed references to argument type orsubstituted them with argument name. Argu-ment names are not accessible in Java bytecodeand therefore the argument name provided by PA-PRIKA is actually the argument type.

Finally, we added the relationship

(variable|argument)-IS_OF_TYPE->(class)

by finding classes whose name matched the argu-ment name or variable type.

After these modifications of the database andqueries, 19 of the 34 GraphifySwift code smellqueries could be used on the Android app databaseproduced by PAPRIKA.

The code smell queries that had to be excludedcontained metrics or attributes that were not pro-vided by PAPRIKA. We excluded for examplequeries referring to code duplication, maximumnesting depth, number of switch statements andnumber of comments.

For the analysis of Android apps we calculatednew thresholds based on the apps that we anal-ysed. The list of iOS and Android thresholds is in-cluded in the thresholds table2.

3.2. Choice of ApplicationsFor analysis of iOS apps we used the same collab-orative list of open source iOS apps as was used by

2https://figshare.com/articles/conference_contribution/Thresholds_for_iOS_and_Android_code_smell_analysis/13102991

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

82

https://figshare.com/articles/conference_contribution/Thresholds_for_iOS_and_Android_code_smell_analysis/13102991https://figshare.com/articles/conference_contribution/Thresholds_for_iOS_and_Android_code_smell_analysis/13102991https://figshare.com/articles/conference_contribution/Thresholds_for_iOS_and_Android_code_smell_analysis/13102991

Rahkema et al. [5] and whose older version wasused by Habchi et al. [3]. The final set of success-fully analysed apps was the same as in [5] and in-cluded 273 open source iOS apps.

For analysis of Android apps we took the list ofapps provided by Habchi et al. [3]. Since the listonly included app package names, we queried All-FreeAPK api3 to find and download these apps. Wedecided to search AllFreeAPK instead of GitHub,as PAPRIKA uses APKs for analysis and this waywe were able to skip the step of compiling theseapps. Later during the analysis we needed to dis-card some of the very big apps due to performanceissues. In total we included 694 open source An-droid apps in our analysis.

3.3. Data AnalysisTo answer RQ1, we checked whether any of the 19identified code smells occurred in at least one appon each platform.

To answer RQ2, we calculated the densities ofcode smells for both iOS and Android apps andcompared these. Code smell density was calcu-lated by counting the number of code smells (totaland per code smell type) and dividing by the num-ber app instructions.

To answer RQ3, we had to perform several cal-culations. To calculate the relative frequencies ofcode smells per code smell type on each platform,we counted the code smells of a type in all appsand divided by the total code smell count. We didthis per platform. To calculate the code smell dis-tributions on app and class levels per platform, wecounted how many apps (and classes) contain atleast one code smell of a certain type and then di-vided by the total number of apps (and classes).

4. ResultsWe analysed 273 open source iOS apps usingGraphifySwift and 694 open source Android appsusing PAPRIKA and modified code smell queriesfrom GraphifySwift to answer our research ques-tions. We analyzed the apps with regards to19 code smells: BlobClass, ComplexClass, Cyclic-ClassDependency, DataClass, DataClumpFields,

3https://m.allfreeapk.com/api/

DistortedHierarchy, DivergentChange, Inappro-priateIntimacy, LazyClass, LongMethod, LongPa-rameterList, MiddleMan, ParallelInheritanceHier-archies, PrimitiveObsession, SAPBreaker, Shot-gunSurgery, SpeculativeGeneralityProtocol, Swis-sArmyKnife and TraditionBreaker.

Below, we present and discuss the results foreach research question separately.

RQ 1: Are all types of object-oriented codesmells present in both iOS and Android apps?

When comparing the occurrence of code smellson each platform, we found that 18 of the 19 iden-tified code smells occurred in apps on both plat-forms, i.e., Android and iOS. Code smell Distort-edHierarchy never occurred in iOS apps.

Our result does not fully support Mannan et al.’sexpectation that mobile apps on other platformsthan Android should exhibit the same code smells[2].

RQ 2: Do code smells occur with the samedensity in iOS and Android apps?

The results of our code smell density analysisis shown in Figure 1. Accumulated over all codesmells it turned out that the apps on the iOS plat-form had a density of 41.7 smells/kilo-instructionswhile the apps on Android only had a density of34.4 smells/kilo-instructions. This result is con-trary to what Habchi et al. [3] expected.

Moreover, it can be seen from Figure 1 that thecode smell densities differ between iOS and An-droid. Code smells LazyClass, DivergentChange,PrimitiveObsession and DataClass had a particu-larly high density in iOS apps. On the other hand,code smells LongMethod, LongParameterList andShotgunSurgery were clearly more frequent in An-droid apps. The fact that code smell densitieswere sometimes higher and sometimes lower iniOS apps as compared to Android apps might beexplained by the fact that Android apps tend tohave more of the code smells that correspond tomore complex classes whereas iOS apps tend tohave more of the code smells that correspond tomore simple classes.

RQ 3: Do code smell distributions differ be-tween iOS and Android apps?

Figure 2 shows the relative frequency of codesmell occurrences over all apps on the Androidplatform (blue bars) and the iOS platform (redbars). The results confirm what we had seen when

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

83

https://m.allfreeapk.com/api/

Long

Met

hod

Long

Par

amet

erLi

st

Sho

tgun

Sur

gery

Lazy

Cla

ss

SA

PB

reak

er

Div

erge

ntC

hang

e

Prim

itive

Obs

essi

on

Dat

aClu

mpF

ield

s

Com

plex

Cla

ss

Inap

prop

riate

Intim

acy

Dis

tort

edH

iera

rchy

Blo

bCla

ss

Cyc

licC

lass

Dep

ende

ncy

Spe

cula

tiveG

ener

ality

Pro

toco

l

Dat

aCla

ss

Sw

issA

rmyK

nife

Par

alle

lInhe

ritan

ceH

iera

rchi

es

Trad

ition

Bre

aker

Mid

dleM

an

Cod

e sm

ell c

ount

per

100

0 in

stru

ctio

ns

0

2

4

6

8 Android

iOS

Figure 1: Comparison of code smell densities betweenAndroid (blue) and iOS (red) apps

Long

Met

hod

Long

Par

amet

erLi

st

Sho

tgun

Sur

gery

Lazy

Cla

ss

SA

PB

reak

er

Div

erge

ntC

hang

e

Prim

itive

Obs

essi

on

Dat

aClu

mpF

ield

s

Com

plex

Cla

ss

Inap

prop

riate

Intim

acy

Dis

tort

edH

iera

rchy

Blo

bCla

ss

Cyc

licC

lass

Dep

ende

ncy

Spe

cula

tiveG

ener

ality

Pro

toco

l

Dat

aCla

ss

Sw

issA

rmyK

nife

Par

alle

lInhe

ritan

ceH

iera

rchi

es

Trad

ition

Bre

aker

Mid

dleM

an

% o

f cod

e sm

ells

0

5

10

15

20

25

Android

iOS

Figure 2: Code smell proportions on Android (blue) andiOS (red)

we compared code smell densities: the proportionsof code smells differ between platforms. In addi-tion, we see that code smells are more evenly dis-tributed in iOS apps as compared to Android apps.

Then we analyzed how large the share of smellyapps on each platform is and how large the shareof smelly classes is on each platform. We did theseanalyses for each code smell type separately.

Figures 3 and 4 show the percentages of appsand classes, respectively, containing code smells ofa certain type.

We found that the percentages of smelly appsare relatively similar between platforms. Thebiggest differences occurs for code smell DataClass(79% of iOS apps have at least one affected class

Lazy

Cla

ss

Long

Met

hod

Long

Par

amet

erLi

st

Sho

tgun

Sur

gery

SA

PB

reak

er

Div

erge

ntC

hang

e

Prim

itive

Obs

essi

on

Com

plex

Cla

ss

Dat

aClu

mpF

ield

s

Blo

bCla

ss

Inap

prop

riate

Intim

acy

Dis

tort

edH

iera

rchy

Cyc

licC

lass

Dep

ende

ncy

Spe

cula

tiveG

ener

ality

Pro

toco

l

Dat

aCla

ss

Sw

issA

rmyK

nife

Par

alle

lInhe

ritan

ceH

iera

rchi

es

Trad

ition

Bre

aker

Mid

dleM

an

% o

f app

s w

ith c

ode

smel

l

0

20

40

60

80 Android

iOS

Figure 3: Comparison of code smell frequencies on applevel between Android (blue) and iOS (red)

while only 7% of Android apps are affected), Mid-dleMan (15% of iOS apps are affected but only 1%of Android apps), and DistortedHierarchy (25 % ofAndroid apps are affected but none of the iOS appsis).

We found that the distributions of code smell oc-currences on class level are more different betweenthe platforms than on app level. This result might,again, be explained by the fact that Android appsusually have larger classes and, thus, tend to havemore of the code smells that correspond to morecomplex classes whereas iOS apps tend to havemore compact classes and, thus, tend to have moreof the code smells that correspond to more simpleclasses. This effect is more prominent when doingthe analysis on class level than on app level.

In addition, we analyzed the occurrence of themethod-based code smells LongMethod and Long-ParameterList separately. We found that in iOSapps 9% of methods are considered LongMethodwhile this is the case for 14% of the methods in An-droid apps. In iOS apps 5% of the methods have aLongParameterList while this is the case for 9% ofmethods in Android apps.

5. Threats to ValidityInternal Validity: In our case internal validitymight be affected by how code smells are detectedby the tools used. PAPRIKA has been used in mul-tiple studies [15, 13, 3, 14]. GraphifySwift was in-

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

84

Lazy

Cla

ss

Long

Met

hod

Long

Par

amet

erLi

st

Sho

tgun

Sur

gery

SA

PB

reak

er

Div

erge

ntC

hang

e

Dat

aClu

mpF

ield

s

Com

plex

Cla

ss

Inap

prop

riate

Intim

acy

Prim

itive

Obs

essi

on

Dis

tort

edH

iera

rchy

Blo

bCla

ss

Dat

aCla

ss

Cyc

licC

lass

Dep

ende

ncy

Spe

cula

tiveG

ener

ality

Pro

toco

l

Par

alle

lInhe

ritan

ceH

iera

rchi

es

Sw

issA

rmyK

nife

Trad

ition

Bre

aker

Mid

dleM

an

% o

f cla

sses

with

cod

e sm

ell

0

10

20

30

40Android

iOS

Figure 4: Comparison of code smell frequencies onclass level between Android (blue) and iOS (red)

troduced in [5] and validated by replicating resultsin [3]. We adapted code smell queries defined in[5], but did so by not changing the code smell def-initions themselves.

External Validity: We analysed open sourceapps. For swift the analysis can only be performedif the code of the app is accessible. For Android theanalysis could be performed on apps from the appstore. Therefore for both platforms open sourceapps were chosen. Previously [5] it was shown thatalthough there are some differences between appsthat are on the app store the differences are small.On the iOS platform we only analyzed apps writtenin Swift. Given that Objective-C and Swift code isquite similar, we assume our results extend to appswritten in Objective-C.

Construct Validity: GraphifySwift uses stan-dard definitions of code smells found in literature[5]. In code smell queries we use thresholds calcu-lated based on the app set analysed. Using thresh-olds is a common approach for detecting codesmells. We used the same method to determinethresholds as was used by Hecht et al. [13], Habchiet al. [3] and [5]. Thresholds might differ betweenlanguages, but since they are calculated based onthe current set of apps analysed language specificdifferences should be resolved.

Reliability: For iOS analysis we used the samecollaborative list of open source iOS apps writtenin Swift as was used in [5]. All these apps areavailable on GitHub. The list of successfully anal-ysed apps can be found on the tool GitHub page.

GraphifySwift is open source and also available onthe tool GitHub page. For Android analysis weused the list of apps analysed by [3], the list of suc-cessfully analysed apps can be found in the list ofapps4. PAPRIKA is open source and also availableon GitHub. The adapted code smell queries usedfor Android analysis can be found in the list of An-droid code smell queries5.

6. ConclusionMannan et al. [2] analysed the density and dis-tribution of code smells in Android apps. We cal-culated a similar density and distribution for iOSand Android apps and saw that these densities anddistributions were different. Additionally we dis-covered that one of the code smells analysed byMannan et al. was not present in iOS apps.

Habchi et al. [3] compared ratios of code smelloccurrences on iOS and Android. We extendedtheir research by adding additional code smellsto the analysis and found that code smell occur-rences are not always higher in Android apps. Forsome code smells they were higher in iOS apps.This shows that Android apps are not necessar-ily smellier, but different kinds of code smells aremore prevalent depending on the platform.

These results can be interesting for developersmoving from one platform to the other. It can alsobe useful for developers of tools for these plat-forms. We see that the emphasis on which codesmells to look at is different depending on the plat-form.

AcknowledgmentsThis research was partly funded by the EstonianCenter of Excellence in ICT research (EXCITE),the IT Academy Programme for ICT Research De-velopment, the Austrian ministries BMVIT andBMDW, and the Province of Upper Austria un-der the COMET (Competence Centers for Excel-lent Technologies) Programme managed by FFG,

4https://figshare.com/articles/dataset/iOS_and_Android_app_analysis_data/13103012

5https://figshare.com/articles/conference_contribution/GraphifySwift_queries_adapted_for_PAPARIKA_for_Android_code_smell_analysis/13102994

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

85

https://figshare.com/articles/dataset/iOS_and_Android_app_analysis_data/13103012https://figshare.com/articles/dataset/iOS_and_Android_app_analysis_data/13103012https://figshare.com/articles/conference_contribution/GraphifySwift_queries_adapted_for_PAPARIKA_for_Android_code_smell_analysis/13102994https://figshare.com/articles/conference_contribution/GraphifySwift_queries_adapted_for_PAPARIKA_for_Android_code_smell_analysis/13102994https://figshare.com/articles/conference_contribution/GraphifySwift_queries_adapted_for_PAPARIKA_for_Android_code_smell_analysis/13102994

and by the group grant PRG887 of the Estonian Re-search Council. We thank Rudolf Ramler for thethorough review of a previous version of this pa-per.

References[1] M. Fowler, Refactoring: improving the de-

sign of existing code, Addison-Wesley Profes-sional, 2018.

[2] U. A. Mannan, I. Ahmed, R. A. M. Almurshed,D. Dig, C. Jensen, Understanding code smellsin android applications, in: 2016 IEEE/ACMInt’l Conf. on Mobile Softw. Eng. and Systems(MOBILESoft), IEEE, 2016, pp. 225–236.

[3] S. Habchi, G. Hecht, R. Rouvoy, N. Moha,Code smells in ios apps: How do they com-pare to android?, in: 2017 IEEE/ACM 4thInt’l Conf. on Mobile Softw. Eng. and Systems(MOBILESoft), IEEE, 2017, pp. 110–121.

[4] G. Hecht, An approach to detect android an-tipatterns, in: Proc. of the 37th Int’l Conf. onSoftware Engineering-Volume 2, IEEE Press,2015, pp. 766–768.

[5] K. Rahkema, D. Pfahl, Empirical study oncode smells in ios applications, in: Proc.of the IEEE/ACM 7th Int’l Conf. on MobileSoftw. Eng. and Systems, MOBILESoft ’20,Association for Computing Machinery, NewYork, NY, USA, 2020, p. 61–65.

[6] F. Khomh, M. Di Penta, Y.-G. Gueheneuc,An exploratory study of the impact of codesmells on software change-proneness, in:2009 16th Working Conf. on Reverse Engi-neering, IEEE, 2009, pp. 75–84.

[7] S. Olbrich, D. S. Cruzes, V. Basili, N. Zaz-worka, The evolution and impact of codesmells: A case study of two open source sys-tems, in: 2009 3rd Int’l symposium on empir-ical software engineering and measurement,IEEE, 2009, pp. 390–400.

[8] M. Linares-Vásquez, S. Klock, C. McMillan,A. Sabané, D. Poshyvanyk, Y.-G. Guéhéneuc,Domain matters: bringing further evidenceof the relationships among anti-patterns, ap-plication domains, and quality-related met-rics in java mobile apps, in: Proc. of the22nd Int’l Conf. on Program Comprehension,

ACM, 2014, pp. 232–243.[9] M. Tufano, F. Palomba, G. Bavota, R. Oliveto,

M. Di Penta, A. De Lucia, D. Poshyvanyk,When and why your code starts to smell bad,in: Proc. of the 37th Int’l Conf. on SoftwareEngineering-Volume 1, IEEE Press, 2015, pp.403–414.

[10] T. Sharma, Extending Maintainability Analy-sis Beyond Code Smells, Ph.D. thesis, 2019.

[11] M. Gottschalk, J. Jelschen, A. Winter, Savingenergy on mobile devices by refactoring., in:EnviroInfo, 2014, pp. 437–444.

[12] M. Ghafari, P. Gadient, O. Nierstrasz, Securitysmells in android, in: 2017 IEEE 17Th Int’lWorking Conf. on Source Code Analysis andManipulation (SCAM), IEEE, 2017, pp. 121–130.

[13] G. Hecht, O. Benomar, R. Rouvoy, N. Moha,L. Duchien, Tracking the software quality ofandroid applications along their evolution (t),in: 2015 30th IEEE/ACM Int’l Conf. on Auto-mated Softw. Eng. (ASE), IEEE, 2015, pp. 236–247.

[14] B. G. Mateus, M. Martinez, An empiricalstudy on quality of android applications writ-ten in kotlin language, Empirical SoftwareEngineering (2018) 1–38.

[15] G. Hecht, R. Rouvoy, N. Moha, L. Duchien,Detecting antipatterns in android apps, in:Proc. of the Second ACM Int’l Conf. on Mo-bile Softw. Eng. and Systems, IEEE Press,2015, pp. 148–149.

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

86

1 Introduction2 Related Work3 Methods3.1 Code Smell Analysis3.2 Choice of Applications3.3 Data Analysis

4 Results5 Threats to Validity6 Conclusion