Tracking the Software Quality of Android Applications Along Their Evolution (T)

Hecht, G.; Benomar, Omar; Rouvoy, Romain; Moha, Naouel; Duchien, Laurence

doi:10.1109/ase.2015.46

Cited by 95 publications

(105 citation statements)

References 16 publications

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“…Heavy Main Thread Method (HEAVY): this code smell is a composition of 3 similar Android smells: Heavy Service Start [27], Heavy BroadcastReceiver [27], [28], and Heavy AsyncTask [27], [57]. The three code smells are defined as Android methods that contain heavy processing and are tied to the main-thread.…”

Section: B Catalog Of Ios Code Smellsmentioning

confidence: 99%

“…PAPRIKA [28] is a tooled approach that detects OO and Android smells in Android apps. It first transforms the input app into a quality model, which is stored in a database graph, and then applies queries onto these graphs to detect the occurrences of code smells.…”

Section: Adapting Paprika For Iosmentioning

confidence: 99%

“…More details about the PAPRIKA model are given in [28]. The graph model of PAPRIKA was originally designed to reflect the core components of any Android app, and consequently it is based on the Java language elements.…”

Section: B Paprika Model For Iosmentioning

confidence: 99%

“…Secondly, we build on PAPRIKA [28], a tooled approach that detects OO and Android smells in Android apps. Specifically, our study exploits an extension of PAPRIKA to analyze iOS apps developed with Objective-C or Swift languages.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Habchi

Hecht

Rouvoy

et al. 2017

2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)

Self Cite

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Section: B Catalog Of Ios Code Smellsmentioning

confidence: 99%

Section: Adapting Paprika For Iosmentioning

confidence: 99%

Section: B Paprika Model For Iosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Habchi

Hecht

Rouvoy

et al. 2017

2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)

Self Cite

View full text Add to dashboard Cite

“…This includes defect prediction [15], effort prediction [55], dependency analysis [50], developer social networks [54], and other topics related to the field of software evolution. The rise of this research area resulted in many tools for mining repositories [24,25], data sets generated by researchers [64], analytics performed by researchers [20,36], and insights into the process of software evolution [38,47]. Due to the diversity of approaches, researchers face five major problems, when it comes to the external validity of the results.…”

Section: Introductionmentioning

confidence: 99%

Adressing problems with external validity of repository mining studies through a smart data platform

Trautsch

Herbold

Makedonski

et al. 2016

Proceedings of the 13th International Conference on Mining Software Repositories

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Research in software repository mining has grown considerably the last decade. Due to the data-driven nature of this venue of investigation, we identified several problems within the current state-of-the-art that pose a threat to the external validity of results. The heavy re-use of data sets in many studies may invalidate the results in case problems with the data itself are identified. Moreover, for many studies data and/or the implementations are not available, which hinders a replication of the results and, thereby, decreases the comparability between studies. Even if all information about the studies is available, the diversity of the used tooling can make their replication even then very hard. Within this paper, we discuss a potential solution to these problems through a cloud-based platform that integrates data collection and analytics. We created the prototype SmartSHARK that implements our approach. Using SmartSHARK, we collected data from several projects and created different analytic examples. Within this article, we present Smart-SHARK and discuss our experiences regarding the use of SmartSHARK and the mentioned problems.

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The evolution of open-source mobile applications: An empirical study

Guo

Shen

et al. 2017

J Softw Evol Proc

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Now, mobile applications grow at an exponential speed and their evolution activities are very active, while there is little research on the evolution of mobile apps. To have a better understanding of the evolution of mobile apps and find similarities or patterns in their evolution process, we conduct an empirical study on long spans in the lifetime of 8 typical open‐source mobile apps, which covers 348 official releases. First, we try to verify whether Lehman's laws still apply to mobile apps or not, extract a variety of metrics of the apps, and use statistical hypothesis testing to validate these laws. We find enough data that support a subset of Lehman's laws, while the rest do not. Second, we make some novel observations, eg, the growth of mobile apps is nonsmooth, and some versions of the apps have a great growth in their evolution. Enough data confirming that software instability increases great with the addition of third‐party method invocations, and automatic build and manage tool based on contract is introduced into project as apps continue evolving.

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Tracking the Software Quality of Android Applications Along Their Evolution (T)

Cited by 95 publications

References 16 publications

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

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

Adressing problems with external validity of repository mining studies through a smart data platform

The evolution of open-source mobile applications: An empirical study

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