On the Evolution of Mobile App Complexity

Gao, Jun; Li, Li; Bissyandé, Tegawendé F.; Klein, Jacques

doi:10.1109/iceccs.2019.00029

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…Considering, that this information is not always present in submitted bug reports (missing in 30% of the cases), techniques that are able to infer, prioritize environmental settings (e.g., device and platform versions) are needed to help drive research on more advanced automated mobile bug report analysis techniques. Furthermore, considering that apps are released frequently [44], [45] and bug reports do not always contain the associated app version, it would be beneficial to automatically infer the version of the app associated with a bug report. This task could be done by automatically by mapping bug report information into GUI components or code entities in the app.…”

Section: Rq 4 : Do Discussion Threads Of Reproducible Bug Reports Con...mentioning

confidence: 99%

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

Johnson

Mahmud

Wendland

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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One of the key tasks related to ensuring mobile app quality is the reporting, management, and resolution of bug reports. As such, researchers have committed considerable resources toward automating various tasks of the bug management process for mobile apps, such as reproduction and triaging. However, the success of these automated approaches is largely dictated by the characteristics and properties of the bug reports they operate upon. As such, understanding mobile app bug reports is imperative to drive the continued advancement of report management techniques. While prior studies have examined high-level statistics of large sets of reports, we currently lack an in-depth investigation of how the information typically reported in mobile app issue trackers relates to the specific details generally required to reproduce the underlying failures.In this paper, we perform an in-depth analysis of 180 reproducible bug reports systematically mined from Android apps on GitHub and investigate how the information contained in the reports relates to the task of reproducing the described bugs. In our analysis, we focus on three pieces of information: the environment needed to reproduce the bug report, the steps to reproduce (S2Rs), and the observed behavior. Focusing on this information, we characterize failure types, identify the modality used to report the information, and characterize the quality of the information within the reports. We find that bugs are reported in a multi-modal fashion, the environment is not always provided, and S2Rs often contain missing or non-specific enough information. These findings carry with them important implications on automated bug reproduction techniques as well as automated bug report management approaches more generally.

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Section: Rq 4 : Do Discussion Threads Of Reproducible Bug Reports Con...mentioning

confidence: 99%

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

Johnson

Mahmud

Wendland

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…First, we use 42 source code metrics that have been defined and used in previous experiments related, for instance, to the assessment of the overall quality of the software [15], [16], [17], [18], [19], [20], [21], [22]. These metrics are grouped in different categories like inheritance, communication, and complexity and readability.…”

Section: B Instantiating Migrationexp For Supporting Java To Kotlin M...mentioning

confidence: 99%

An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

Mateus,

Martinez,

Kolski

2021

Preprint

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In 2017, Google announced Kotlin as an official Android programming language, and more recently, as the preferred programming language to build applications. These facts motivated developers to migrate their applications, which is challenging because each migrated piece of code must be tested after the migration to ensure it preserves the expected behavior. Due to the interoperability between Java and Kotlin, most developers decided to migrate their applications gradually. Thus, developers have to decide which file(s) to migrate first on each migration step. However, there are no tools available to help developers make these choices. This paper presents an approach to support a gradual migration of Android applications that given a version of an application written in Java and eventually, in Kotlin, it suggests the most convenient files to migrate. To this end, we built a large-scale corpus of open-source projects that migrated Java files to Kotlin. Then, we trained a learning to rank model using the information extracted from these projects. To validate our model, we verify whether these recommendations made by them correspond to real migrations. The results showed our approach modestly outperforms random approaches. Since most Android applications are written in Java, we conclude that our approach may significantly impact Android applications' development. Therefore, we consider this result is the first step into long-term research towards a model capable of predicting precisely file-level migration, establishing the initial baseline on file migrations.

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“…App complexity is an elusive concept. Yet, in the literature, there are various studies that propose metrics to measure some form of complexity and attempt to show its correlation with app quality and maintainability (Jošt et al 2013;Gao et al 2019b). We undertake to investigate our research question based on these common metrics from the literature (Chidamber and Kemerer 1994).…”

Section: Do Covid-related Apps Have Specific Characteristics?mentioning

confidence: 99%

A First Look at Android Applications in Google Play related to Covid-19

Samhi¹,

Allix²,

Bissyandé³

et al. 2020

Preprint

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Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the Covid-19, with apps released as early as February 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.

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On the Evolution of Mobile App Complexity

Cited by 14 publications

References 31 publications

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

A First Look at Android Applications in Google Play related to Covid-19

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