poster

Software clustering by example

Authors:

Martin Faunes,

Marouane Kessentini,

Houari SahraouiAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 245 - 246

https://doi.org/10.1145/2001858.2001996

Published: 12 July 2011 Publication History

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Abstract

We model software clustering problems in a setting, where elements of a software system form a graph to be partitioned in order to derive high-level abstractions. We extend this formulation in a way that the graph partitioning solutions are evaluated by the degree of their conformance with past clustering cases given as examples. We provide a concrete illustration of this formulation with the problem of object identification in procedural code, for which we obtained better results than a clustering approach.

References

[1]

H. Abdeen, S. Ducasse, H. Sahraoui, and I. Alloui. Automatic package coupling and cycle minimization. In Proceedings of the 2009 16th Working Conference on Reverse Engineering, pages 103--112, 2009.

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Google Scholar

[2]

I. Czibula and G. Serban. Improving systems design using a clustering approach. Int. Journal of Computer Science and Network Security, 6(12):40--49, 2006.

Google Scholar

[3]

M. Harman, R. M. Hierons, and M. Proctor. A new representation and crossover operator for search-based optimization of software modularization. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1351--1358, 2002.

Digital Library

Google Scholar

[4]

B. S. Mitchell. A heuristic search approach to solving the software clustering problem. PhD thesis, 2002.

Digital Library

Google Scholar

[5]

H. Sahraoui, H. Lounis, W. Melo, and H. Mili. A concept formation based approach to object identification in procedural code. Automated Software Engg., 6:387--410, 1999.

Digital Library

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Cited By

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Srinivas CRao CAljawarneh SAl-Shargabi B(2015)A Feature Vector Based Approach for Software Component Clustering and Reuse Using K-meansProceedings of the The International Conference on Engineering & MIS 201510.1145/2832987.2833080(1-5)Online publication date: 24-Sep-2015
https://dl.acm.org/doi/10.1145/2832987.2833080
Srinivas CRadhakrishna VRao CAljawarneh SAl-Shargabi B(2015)Clustering Software Project Components for Strategic Decisions and Building Reuse LibrariesProceedings of the The International Conference on Engineering & MIS 201510.1145/2832987.2833075(1-5)Online publication date: 24-Sep-2015
https://dl.acm.org/doi/10.1145/2832987.2833075

Index Terms

Software clustering by example
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

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Information & Contributors

Information

Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Srinivas CRao CAljawarneh SAl-Shargabi B(2015)A Feature Vector Based Approach for Software Component Clustering and Reuse Using K-meansProceedings of the The International Conference on Engineering & MIS 201510.1145/2832987.2833080(1-5)Online publication date: 24-Sep-2015
https://dl.acm.org/doi/10.1145/2832987.2833080
Srinivas CRadhakrishna VRao CAljawarneh SAl-Shargabi B(2015)Clustering Software Project Components for Strategic Decisions and Building Reuse LibrariesProceedings of the The International Conference on Engineering & MIS 201510.1145/2832987.2833075(1-5)Online publication date: 24-Sep-2015
https://dl.acm.org/doi/10.1145/2832987.2833075

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A modified genetic algorithm for software clustering problem

Clustering large software systems at multiple layers

Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach