Code Smells in iOS Apps: How Do They Compare to Android?

Habchi, Sarra; Hecht, G.; Rouvoy, Romain; Moha, Naouel

doi:10.1109/mobilesoft.2017.11

Cited by 32 publications

(46 citation statements)

References 23 publications

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“…Hypothesis 1 (H1): Code smell introductions and removals are isolated practices. This hypothesis is supported by studies that suggested that the presence of code smells is the result of the actions of only some developers [18], [19].…”

Section: Introductionmentioning

confidence: 77%

“…With 85.9 % of the market share, Android is the most popular mobile operating system as of 2018. 1 Moreover, more Android apps are available in opensource repositories compared to other mobile platforms [19]. On top of that, both development and research communities proposed tools to analyse the source code of Android apps and detect their code smells [4], [29], [38].…”

Section: A Context Selectionmentioning

confidence: 99%

“…They only approached this topic in their explanatory hypotheses when discussing the possible factors of mobile code smells accumulation. In particular, they hypothesised that some mobile developers who lack experience and knowledge about the framework, are responsible for the accrual of mobile code smells [18], [19], [26]. Our study builds on these hypotheses and addresses the developers role with a large scale analysis.…”

Section: Introductionmentioning

confidence: 98%

“…Hypothesis 2 (H2): There is an inverse correlation between code smell introductions and removals among developers. This hypothesis is motivated by the reasoning that some bad developers who are unaware about code smells will tend to introduce and never remove them and vice versa [18], [19], [26], [29]. Hypothesis 3 (H3): For each code smell type, there is an inverse correlation between code smell introductions and removals among developers.…”

Section: Introductionmentioning

confidence: 99%

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The Rise of Android Code Smells: Who is to Blame?

Habchi

Moha

Rouvoy

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

Self Cite

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The rise of mobile apps as new software systems led to the emergence of new development requirements regarding performance. Development practices that do not respect these requirements can seriously hinder app performances and impair user experience, they qualify as code smells. Mobile code smells are generally associated with inexperienced developers who lack knowledge about the framework guidelines. However, this assumption remains unverified and there is no evidence about the role played by developers in the accrual of mobile code smells. In this paper, we therefore study the contributions of developers related to Android code smells. To support this study, we propose SNIFFER, an open-source toolkit that mines Git repositories to extract developers' contributions as code smell histories. Using SNIFFER, we analysed 255k commits from the change history of 324 Android apps. We found that the ownership of code smells is spread across developers regardless of their seniority. There are no distinct groups of code smell introducers and removers. Developers who introduce and remove code smells are mostly the same.

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Section: Introductionmentioning

confidence: 77%

Section: A Context Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Rise of Android Code Smells: Who is to Blame?

Habchi

Moha

Rouvoy

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

Self Cite

View full text Add to dashboard Cite

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“…Apple App Store dan Google Play Store berhasil menaklukkan perangkat seluler. Namun, keberhasilan ini juga menantang pengembang aplikasi untuk menerbitkan aplikasi berkualitas tinggi agar tetap menarik dan memuaskan pengguna [6]. Smartphone dapat dibedakan menjadi dua kategori umum yaitu: Berdasarkan manufaktur, Terdapat banyak macam smartphone yang diproduksi oleh sebagian besar manufaktur seperti, Apple, Samsung, HTC, LG, Nokia, Blackberry, Sony, Motorola, dan lain-lain; (2) Berdasarkan sistem operasi smartphone, Ada 3 sistem operasi utama pada smartphone: Google Android adalah sistem operasi yang paling populer pada smartphone, dan terdapat ratusan model smartphone dari puluhan produsen yang menggunakannya., Apple iOS, sistem yang digunakan pada iPhone dan iPad adalah sistem yang sederhana.…”

Section: Sistem Operasi Smartphoneunclassified

Perbandingan Fitur Smartphone, Pemanfaatan Dan Tingkat Usability Pada Android Dan iOS Platforms

Arnomo

Hendra²

2019

InfoTekJar

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Android dan iOS nowadays have become the most dominating operating system in smartphone markets. These two operating system continue to grow rapidly in terms of technology as well as the number of devices that exist in the world. However, consumers are still lacking of understanding on how to compare these two operating systems from various of aspects. So, this research is being conducted with the purpose to learn about the comparison of these two operating system in terms of features, utilizations and usability. Method that is being used in this research is descriptive analysis which in comparison of features and utilizations are using documentation and observation as data collection technique. For usability comparison is using questionnaire technique. Result of the research has shown that Android and iOS each have their unique features such as Google Hangouts with i-Message and Smart Caller ID with Facetime. Afterwards, comparison of utilizations has shown that Android is mostly being used for communication as for iOS more for information searching. Meanwhile, for descriptive result of usability has shown that Android and iOS have the same level of ease of use which is good. CORRESPONDENCE

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iPerfDetector: Characterizing and detecting performance anti-patterns in iOS applications

2019

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Performance issues in mobile applications (i.e., apps) often have a direct impact on the user experience. However, due to limited testing resources and fast-paced software development cycles, many performance issues remain undiscovered when the apps are released. As found by a prior study, these performance issues are one of the most common complaints that app users have. Unfortunately, there is a limited support to help developers avoid or detect performance issues in mobile apps.In this thesis, we conduct an empirical study on performance issues in iOS apps written in Swift language. To the best of our knowledge, this is the first study on performance issues of apps on the iOS platform. We manually studied 235 performance issues that are collected from four open source iOS apps. We found that most performance issues in iOS apps are related to inefficient UI design, memory issues, and inefficient thread handling. We also manually uncovered four performance anti-patterns that recurred in the studied issue reports. To help developers avoid these performance anti-patterns in the code, we implemented a static analysis tool called iPerfDetector. We evaluated iPerfDetector on eight open source and three commercial apps. iPerfDetector successfully detected 34 performance anti-pattern instances in the studied apps, where 31 of them are already confirmed and accepted by developers as potential performance issues. Our case study on the performance impact of the anti-patterns shows that fixing the anti-pattern may improve the performance (i.e., response time, GPU, or CPU) of the workload by up to 80%.iii I would like to thank my supervisor, Dr. Tse-hsun (Peter) Chen, whom without his persistent help and his guidance, this dissertation would not have been possible. I would also like to thank my committee members, for their suggestions and comments. vi Contents List of Figures ix List of Tables xTable 5.3 GPU usage (percentage) changes in median, average, and standard deviation, after resolving the BLER and TREF anti-pattern instances. . . . . . . . . . . . . . Table 5.4 CPU usage changes (percentage) in median, average, and standard deviation, and response time changes (percentage) after resolving the DM anti-pattern

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Code Smells in iOS Apps: How Do They Compare to Android?

Cited by 32 publications

References 23 publications

The Rise of Android Code Smells: Who is to Blame?

The Rise of Android Code Smells: Who is to Blame?

Perbandingan Fitur Smartphone, Pemanfaatan Dan Tingkat Usability Pada Android Dan iOS Platforms

iPerfDetector: Characterizing and detecting performance anti-patterns in iOS applications

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