Reference : Lightweight Detection of Android-specific Code Smells: the aDoctor Project

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/29379

Title :	Lightweight Detection of Android-specific Code Smells: the aDoctor Project
Language :	English
Author, co-author :	Palomba, Fabio [University of Salerno > Computer Science]
	Di Nucci, Dario [University of Salerno > Computer Science]
	Panichella, Annibale [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]
Publication date :	21-Feb-2017
Main document title :	Proceedings of the 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2017)
Publisher :	IEEE
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	International Conference on Software Analysis, Evolution, and Reengineering
Event date :	from 21-02-2017 to 24-02-2016
Keywords :	[en] Android-specific Code Smells ; Detection Tool ; Empirical Study
Abstract :	[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.
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/10993/29379

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