Implications of deep learning for the automation of design patterns organization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

47 Scopus Citations
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  • Awais Ahmad
  • Salvatore Cuomo
  • Francesco Piccialli
  • Gwanggil Jeon
  • Adnan Akhunzada

Related Research Unit(s)


Original languageEnglish
Pages (from-to)256-266
Journal / PublicationJournal of Parallel and Distributed Computing
Online published20 Jul 2017
Publication statusPublished - Jul 2018


Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier's performance in terms of organization of design patterns.

Research Area(s)

  • Classifiers, Deep learning, Design patterns, Feature set, Performance

Citation Format(s)