Reference : Lightweight Detection of Android-specific Code Smells: the aDoctor Project
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/29379
Lightweight Detection of Android-specific Code Smells: the aDoctor Project
English
Palomba, Fabio [University of Salerno > Computer Science]
Di Nucci, Dario [University of Salerno > Computer Science]
Panichella, Annibale mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Zaidman, Andy [Delft University of Technology > EWI]
De Lucia, Andre [University of Salerno > Computer Science]
21-Feb-2017
Proceedings of the 39th International Conference on Software Engineering (ICSE 2017)
IEEE
Yes
No
International
International Conference on Software Analysis, Evolution, and Reengineering
from 21-02-2017 to 24-02-2016
[en] Android-specific Code Smells ; Detection Tool ; Empirical Study
[en] Code smells are symptoms of poor design solutions applied by programmers during the development of software systems. While the research community devoted a lot of effort to studying and devising approaches for detecting the traditional code smells defined by Fowler, little knowledge and support is available for an emerging category of Mobile app code smells. Recently, Reimann etal proposed a new catalogue of Android-specific code smells that may be a threat for the maintainability and the efficiency of Android applications. However, current tools working in the context of Mobile apps provide limited support and, more importantly, are not available for developers interested in monitoring the quality of their apps. To overcome these limitations, we propose a fully automated tool, coined aDoctor, able to identify 15 Android-specific code smells from the catalogue by Reimann et al. An empirical study conducted on the source code of 18 Android applications reveals that the proposed tool reaches, on average, 98% of precision and 98% of recall. We made aDoctor publicly available.
Researchers ; Professionals
http://hdl.handle.net/10993/29379

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