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ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications


Year of publication


Published in

Foundations of Computing and Decision Sciences

Journal year: 2022 | Journal volume: vol. 47 | Journal number: no. 1

Article type

scientific article

Publication language


  • Software Vulnerabilities
  • Code Smells
  • Android
  • ANN
  • Apriori Algorithm
  • Deep Learning
  • Machine Learning

EN There has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security issues related to code smells that lead to vulnerabilities in software. This research focuses on the vulnerability detection in Android applications which consists of code smells. A multi-layer perceptron-based ANN model is generated for detection of software vulnerabilities and has a precision value of 74.7% and 79.6% accuracy with 2 hidden layers. The focus is laid on 1390 Android classes and involves association mining of the software vulnerabilities with android code smells using APRIORI algorithm. The generated ANN model The findings represent that Member Ignoring Method (MIM) code smell shows an association with Bean Member Serialization (BMS) vulnerability having 86% confidence level and 0.48 support value. An algorithm has also been proposed that would help developers in detecting software vulnerability in the smelly source code of an android applications at early stages of development.

Date of online publication


Pages (from - to)

3 - 26




License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Points of MNiSW / journal


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