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Soft Computing

, Volume 23, Issue 24, pp 13433–13448 | Cite as

A methodology to rank the design patterns on the base of text relevancy

  • Shahid HussainEmail author
  • Jacky Keung
  • Mohammad Khalid Sohail
  • Arif Ali KhanEmail author
  • Manzoor Ilahi
  • Ghufran Ahmad
  • Muhammad Rafiq Mufti
  • Muhammad Asim Noor
Methodologies and Application
  • 120 Downloads

Abstract

Several software design patterns have cataloged either with canonical or as variants to solve a recurring design problem. However, novice designers mostly adopt patterns without considering their ground reality and relevance to design problems, which causes to increase the development and maintenance efforts. The existing automated systems to select the design patterns need either high computing effort and time for the formal specification or precise learning through the training of several classifiers with large sample size to select the right design patterns realized on the base of their ground reality. In order to discuss this issue, we propose a method on the base of a supervised learning technique named ‘Learning to Rank’, to rank the design patterns via the text relevancy with the description of the given design problems. Subsequently, we also propose an evaluation model to assess the effectiveness of the proposed method. We evaluate the efficacy of the proposed method in the context of several design pattern collections and relevant design problems. The promising experimental results indicate the applicability of the proposed method as a recommendation system to select the right design pattern(s).

Keywords

Software design patterns Text mining Learning to rank Performance Classification 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Since in the article there is no human involvement for data gathering, consequently informed consent is not required.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Shahid Hussain
    • 1
    Email author
  • Jacky Keung
    • 2
  • Mohammad Khalid Sohail
    • 1
  • Arif Ali Khan
    • 3
    Email author
  • Manzoor Ilahi
    • 1
  • Ghufran Ahmad
    • 1
  • Muhammad Rafiq Mufti
    • 4
  • Muhammad Asim Noor
    • 1
  1. 1.CUIslamabadPakistan
  2. 2.City University of Hong KongHong KongChina
  3. 3.Nanjing UniversityNanjingChina
  4. 4.CUVehariPakistan

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