TY - GEN
T1 - Lightweight detection of Android-specific code smells: The aDoctor project
AU - Palomba, Fabio
AU - Di Nucci, Dario
AU - Panichella, Annibale
AU - Zaidman, Andy
AU - De Lucia, Andrea
PY - 2017
Y1 - 2017
N2 - 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 et al. 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.
AB - 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 et al. 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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85018413794&partnerID=MN8TOARS
U2 - 10.1109/SANER.2017.7884659
DO - 10.1109/SANER.2017.7884659
M3 - Conference contribution
BT - SANER 2017 - 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering
ER -