Standard

Lightweight Detection of Android-specific Code Smells : The aDoctor Project. / Palomba, Fabio; Di Nucci, Dario; Panichella, A.; Zaidman, Andy; De Lucia, Andrea.

Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017. ed. / Martin Pinzger; Gabriele Bavota; Andrian Marcus. Piscataway, NJ : IEEE, 2017. p. 487-491.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Palomba, F, Di Nucci, D, Panichella, A, Zaidman, A & De Lucia, A 2017, Lightweight Detection of Android-specific Code Smells: The aDoctor Project. in M Pinzger, G Bavota & A Marcus (eds), Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017. IEEE, Piscataway, NJ, pp. 487-491, SANER 2017, Klagenfurt, Austria, 21/02/17. https://doi.org/10.1109/SANER.2017.7884659

APA

Palomba, F., Di Nucci, D., Panichella, A., Zaidman, A., & De Lucia, A. (2017). Lightweight Detection of Android-specific Code Smells: The aDoctor Project. In M. Pinzger, G. Bavota, & A. Marcus (Eds.), Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017 (pp. 487-491). Piscataway, NJ: IEEE. https://doi.org/10.1109/SANER.2017.7884659

Vancouver

Palomba F, Di Nucci D, Panichella A, Zaidman A, De Lucia A. Lightweight Detection of Android-specific Code Smells: The aDoctor Project. In Pinzger M, Bavota G, Marcus A, editors, Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017. Piscataway, NJ: IEEE. 2017. p. 487-491 https://doi.org/10.1109/SANER.2017.7884659

Author

Palomba, Fabio ; Di Nucci, Dario ; Panichella, A. ; Zaidman, Andy ; De Lucia, Andrea. / Lightweight Detection of Android-specific Code Smells : The aDoctor Project. Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017. editor / Martin Pinzger ; Gabriele Bavota ; Andrian Marcus. Piscataway, NJ : IEEE, 2017. pp. 487-491

BibTeX

@inproceedings{349ca254ea0d42109169791e3652a6ab,
title = "Lightweight Detection of Android-specific Code Smells: The aDoctor Project",
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.",
keywords = "Android-specific Code Smells, Detection Tool, Empirical Study",
author = "Fabio Palomba and {Di Nucci}, Dario and A. Panichella and Andy Zaidman and {De Lucia}, Andrea",
year = "2017",
doi = "10.1109/SANER.2017.7884659",
language = "English",
pages = "487--491",
editor = "Martin Pinzger and Gabriele Bavota and Andrian Marcus",
booktitle = "Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Lightweight Detection of Android-specific Code Smells

T2 - The aDoctor Project

AU - Palomba, Fabio

AU - Di Nucci, Dario

AU - Panichella, A.

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 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.

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 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.

KW - Android-specific Code Smells

KW - Detection Tool

KW - Empirical Study

UR - http://resolver.tudelft.nl/uuid:349ca254-ea0d-4210-9169-791e3652a6ab

U2 - 10.1109/SANER.2017.7884659

DO - 10.1109/SANER.2017.7884659

M3 - Conference contribution

SP - 487

EP - 491

BT - Proceedings - 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017

A2 - Pinzger, Martin

A2 - Bavota, Gabriele

A2 - Marcus, Andrian

PB - IEEE

CY - Piscataway, NJ

ER -

ID: 32869403