Lightweight Detection of Android-specific Code Smells

The aDoctor Project

Conference Paper (2017)
Author(s)

Fabio Palomba (University of Salerno, TU Delft - Software Engineering)

D. Di Nucci (University of Salerno)

Annibale Panichella (Université du Luxembourg)

A.E. Zaidman (TU Delft - Software Engineering)

Andrea De Lucia (University of Salerno)

Research Group
Software Engineering
Copyright
© 2017 F. Palomba, D. Di Nucci, A. Panichella, A.E. Zaidman, Andrea De Lucia
DOI related publication
https://doi.org/10.1109/SANER.2017.7884659
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 F. Palomba, D. Di Nucci, A. Panichella, A.E. Zaidman, Andrea De Lucia
Research Group
Software Engineering
Pages (from-to)
487-491
ISBN (electronic)
978-1-5090-5501-2
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

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 isavailable for an emerging category of Mobile app code smells. Recently, Reimann et al. proposed a new catalogue of Androidspecific 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 conductedon 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.

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