Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)

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Knowledge Discovery for Design Pattern Selection

Authors
Poonam Ponde1, *, Manisha Bharambe2, Kavita Khobragade3, Manisha Suryawanshi4
1Nowrosjee Wadia College, Pune, India
2MES Abasaheb Garware College, Pune, India
3Fergusson College, Pune, India
4Modern College of Arts, Science and Commerce, Pune, India
*Corresponding author. Email: poonamponde@nowrosjeewadiacollege.edu.in
Corresponding Author
Poonam Ponde
Available Online 1 May 2023.
DOI
10.2991/978-94-6463-136-4_7How to use a DOI?
Keywords
Design Pattern; Knowledge Discovery; Clustering; Software Engineering
Abstract

Design patterns are useful Software Engineering tools that enable the reuse of expert solutions to recurring problems. There are a large number of patterns, spread in multiple catalogs and in heterogeneous formats. Selecting and applying the right design pattern requires an in-depth understanding of patterns and their classification. The solution architects must either rely on the advice of experts or laboriously go through the available literature to find the relevant patterns. Pattern applicability will improve if the entire pattern knowledge is available in one place and in a standard format. If the pattern data is augmented with additional knowledge to guide the architect on choosing the right patterns for a particular requirement, it will be immensely useful and productive. The objective of the knowledge discovery process on the design pattern landscape is to extract useful relations and groups of patterns to enable users to select and apply patterns effectively. The present work discusses a model for analyzing existing pattern data, extracting knowledge thereof, and representing this knowledge in a format to enable pattern search and its application.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
Series
Advances in Computer Science Research
Publication Date
1 May 2023
ISBN
10.2991/978-94-6463-136-4_7
ISSN
2352-538X
DOI
10.2991/978-94-6463-136-4_7How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Poonam Ponde
AU  - Manisha Bharambe
AU  - Kavita Khobragade
AU  - Manisha Suryawanshi
PY  - 2023
DA  - 2023/05/01
TI  - Knowledge Discovery for Design Pattern Selection
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 47
EP  - 60
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_7
DO  - 10.2991/978-94-6463-136-4_7
ID  - Ponde2023
ER  -