research-article

Clustering Software Components for Component Reuse and Program Restructuring

Authors:

Chintakindi Srinivas,

Vangipuram Radhakrishna,

C. V. Guru RaoAuthors Info & Claims

ICCC '13: Proceedings of the Second International Conference on Innovative Computing and Cloud Computing

Pages 261 - 266

https://doi.org/10.1145/2556871.2556933

Published: 01 December 2013 Publication History

Abstract

Clustering Software Components for efficient component retrieval is gaining a significant practical importance in the field of software engineering from academic researchers and software industry. Clustering reduces the search space of components by grouping similar entities together thus ensuring reduced time complexity. Finding software components for efficient software reuse is one of the important problems gaining interest from researchers. In this Paper, we first define a similarity function and then give a generalized approach for clustering software components. A component may be a program module or any software document. The objective of component clustering is to form clusters containing high cohesive and low coupling components. Experiments were conducted with Reuters 21578 dataset by considering 70% of documents for training and 30% as test data.

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Cited By

Sarhan QAhmed BBures MZamli K(2022)Software Module Clustering: An In-Depth Literature AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2020.304255348:6(1905-1928)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TSE.2020.3042553
Bandaru RPola SThadem SPendyala KVangipuram RVangipuram S(2021)Design and Analysis of activation functions used in deep learning modelsThe 7th International Conference on Engineering & MIS 202110.1145/3492547.3492575(1-5)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3492547.3492575
Nagaraja ABoregowda UVangipuram RGunupudi R(2021)Design of Gaussian Similarity Measure for Network Anomaly DetectionInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460759(225-230)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460759
Show More Cited By

Index Terms

Clustering Software Components for Component Reuse and Program Restructuring
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
    2. Software post-development issues
      1. Software reverse engineering
  2. Software notations and tools
    1. Software libraries and repositories

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cover image ACM Other conferences

ICCC '13: Proceedings of the Second International Conference on Innovative Computing and Cloud Computing

December 2013

285 pages

ISBN:9781450321198

DOI:10.1145/2556871

General Chairs:
Min Wu
Nanchang University, China
,
Wei Lee
Asia Pacific Human-Computer Interaction Research Center, Hong Kong
,
Program Chairs:
Yiyi Zhouzhou
Azerbaijan State Oil Academy, Azerbaijan
,
Riza Esa
Kuala Lumpur ACM Chapter, Malaysia
,
Xiang Lee
Hong Kong Education Society, Hong Kong

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 December 2013

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ICCC '13: 2013 The Second International Conference on Innovative Computing and Cloud Computing

December 1 - 2, 2013

Wuhan, China

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Cited By

Sarhan QAhmed BBures MZamli K(2022)Software Module Clustering: An In-Depth Literature AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2020.304255348:6(1905-1928)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TSE.2020.3042553
Bandaru RPola SThadem SPendyala KVangipuram RVangipuram S(2021)Design and Analysis of activation functions used in deep learning modelsThe 7th International Conference on Engineering & MIS 202110.1145/3492547.3492575(1-5)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3492547.3492575
Nagaraja ABoregowda UVangipuram RGunupudi R(2021)Design of Gaussian Similarity Measure for Network Anomaly DetectionInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460759(225-230)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460759
Nagaraja ABoregowda UVangipuram R(2021)Fuzzy Feature Similarity Functions for Feature Clustering and Dimensionality ReductionInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460758(219-224)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460758
Vangipuram SAppusamy R(2021)A SURVEY ON SIMILARITY MEASURES AND MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION AND PREDICTIONInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460755(198-204)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460755
Cheruvu AVangipuram R(2021)A Survey of Similarity Measures for Time stamped Temporal DatasetsInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460754(193-197)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460754
Nagaraja AK N SSinha ARAJENDRA KUMAR JNayak P(2021)Fake News Detection Using Machine Learning MethodsInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460753(185-192)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460753
M.S.B PC.V GVangipuram RCheruvu A(2021)Similarity Association Pattern Mining in Transaction DatabasesInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460752(180-184)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460752
Nagaraja ABoregowda UVangipuram R(2021)Regression analysis for network intrusion detectionInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460751(173-179)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460751
Nagaraja ABoregowda UVangipuram R(2021)Study of Detection of DDoS attacks in cloud environment Using Regression AnalysisInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460750(166-172)Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1145/3460620.3460750
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