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International Conference on Recent Developments in Science, Engineering and Technology

REDSET 2019: Data Science and Analytics pp 64–79Cite as

Android Smells Detection Using ML Algorithms with Static Code Metrics

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1229))

Abstract

Mobile applications development rate is predominantly increasing in comparison with the regular applications. These mobile applications prove to be change frequently according to the user requirements. Moreover, these changes in the code base may introduce some bad design practices that are called as bad smells, which can lead to a higher maintenance cost and degrade quality of the software. A very less attention has been given in the detection of code smells in the mobile applications that are also called as android smells. This research contains the rules in combination of software metrics and their threshold values to detect the bad smells in the android applications. The proposed rules are computed using three different machine learning algorithms. This framework has been applied to 2896 instances of the android applications which are open-sourced on GitHub. The android code smells MIM, LIC, DTWC and SL have been considered for the generation of detection rules and are validated using 10-fold cross validation method. The machine learning algorithm JRip furnished the best result for the android smells up to 90% overall precision, which is quite sufficient to justify the results.

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Author information

Authors and Affiliations

  1. ASET, GGS Indraprastha University, New Delhi, India

    Aakanshi Gupta & Vishal Bhat

  2. University School of ICT, GGS Indraprastha University, New Delhi, India

    Bharti Suri

Authors
  1. Aakanshi Gupta
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  2. Bharti Suri
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  3. Vishal Bhat
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Corresponding author

Correspondence to Aakanshi Gupta .

Editor information

Editors and Affiliations

  1. GD Goenka University, Gurugram, India

    Usha Batra

  2. GD Goenka University, Sohna, Haryana, India

    Nihar Ranjan Roy

  3. University of Arkansas, Fayetteville, AR, USA

    Brajendra Panda

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Gupta, A., Suri, B., Bhat, V. (2020). Android Smells Detection Using ML Algorithms with Static Code Metrics. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_6

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  • DOI: https://doi.org/10.1007/978-981-15-5827-6_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5826-9

  • Online ISBN: 978-981-15-5827-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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