Article

Using object-level run-time metrics to study coupling between objects

Authors:

James F. PowerAuthors Info & Claims

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing

Pages 1456 - 1462

https://doi.org/10.1145/1066677.1067010

Published: 13 March 2005 Publication History

Abstract

In this paper we present an investigation into the run-time behaviour of objects in Java programs, using specially adapted coupling metrics. We identify objects from the same class that exhibit non-uniform coupling behaviour when measured dynamically.We define a number of object level run-time metrics, based on the static Chidamber and Kemerer coupling between objects (CBO) measure. These new metrics seek to quantify coupling at different layers of granularity, that is at class-class and object-class level. We outline our method of collecting such metrics and present a study of the programs from the JOlden benchmark suite as an example of their use.A number of statistical techniques, principally agglomerative hierarchical clustering analysis, are used to facilitate the identification of such objects.

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Cited By

Sousa BBigonha MFerreira KFranco G(2024)Evolution of internal dimensions in object‐oriented software–A time series based approachSoftware: Practice and Experience10.1002/spe.331054:6(1034-1073)Online publication date: 21-Jan-2024
https://doi.org/10.1002/spe.3310
Albalooshi FAbbas R(2022)A Sequential Comparative Analysis of Software Change Proneness Prediction Using Machine LearningInternational Journal of Software Innovation10.4018/IJSI.29799310:1(1-16)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSI.297993
Abbas RAlbalooshi FHammad M(2020)Software Change Proneness Prediction Using Machine Learning2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT)10.1109/3ICT51146.2020.9311978(1-7)Online publication date: 20-Dec-2020
https://doi.org/10.1109/3ICT51146.2020.9311978
Show More Cited By

Index Terms

Using object-level run-time metrics to study coupling between objects
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Conferences

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing

March 2005

1814 pages

ISBN:1581139640

DOI:10.1145/1066677

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Editor:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology, Socorro, NM
,
Program Chairs:
Andrea Omicini
Alma Mater Studiorum, Universita di Bologna, Italy
,
Roger L. Wainwright
Univerity of Tulsa, OK

Copyright © 2005 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: 13 March 2005

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March 13 - 17, 2005

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Cited By

Sousa BBigonha MFerreira KFranco G(2024)Evolution of internal dimensions in object‐oriented software–A time series based approachSoftware: Practice and Experience10.1002/spe.331054:6(1034-1073)Online publication date: 21-Jan-2024
https://doi.org/10.1002/spe.3310
Albalooshi FAbbas R(2022)A Sequential Comparative Analysis of Software Change Proneness Prediction Using Machine LearningInternational Journal of Software Innovation10.4018/IJSI.29799310:1(1-16)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSI.297993
Abbas RAlbalooshi FHammad M(2020)Software Change Proneness Prediction Using Machine Learning2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT)10.1109/3ICT51146.2020.9311978(1-7)Online publication date: 20-Dec-2020
https://doi.org/10.1109/3ICT51146.2020.9311978
Kakarontzas GPardalidou CJohn KGrammati PStefanos GChristos D(2018)Improving component coupling information with dynamic profilingProceedings of the 22nd Pan-Hellenic Conference on Informatics10.1145/3291533.3291576(156-161)Online publication date: 29-Nov-2018
https://dl.acm.org/doi/10.1145/3291533.3291576
Tiwari SRathore SReddy YVarma VCleland JBellur USengupta SSharma NLoganathan RSharma RSarkar S(2018)Coupling and Cohesion Metrics for Object-Oriented SoftwareProceedings of the 11th Innovations in Software Engineering Conference10.1145/3172871.3172878(1-11)Online publication date: 9-Feb-2018
https://dl.acm.org/doi/10.1145/3172871.3172878
Ghareb MAllen G(2018)State of the art metrics for aspect oriented programming10.1063/1.5032069(020107)Online publication date: 2018
https://doi.org/10.1063/1.5032069
Kaur NNegi ASingh H(2018)Object Oriented Dynamic Coupling and Cohesion Metrics: A ReviewProceedings of 2nd International Conference on Communication, Computing and Networking10.1007/978-981-13-1217-5_85(861-869)Online publication date: 8-Sep-2018
https://doi.org/10.1007/978-981-13-1217-5_85
Kaur NSingh H(2018)Parametric and Functional-Based Analysis of Object-Oriented Dynamic Coupling MetricsIntegrated Intelligent Computing, Communication and Security10.1007/978-981-10-8797-4_48(469-479)Online publication date: 15-Sep-2018
https://doi.org/10.1007/978-981-10-8797-4_48
Dogra ASingh HSingh PTonelli RCounsell SDestefanis GMurgia ATempero E(2017)Execution trace streaming based real time collection of dynamic metrics using PaaSProceedings of the 8th Workshop on Emerging Trends in Software Metrics10.5555/3106039.3106049(43-48)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.5555/3106039.3106049
Dogra ASingh HSingh P(2017)Execution Trace Streaming Based Real Time Collection of Dynamic Metrics Using PaaS2017 IEEE/ACM 8th Workshop on Emerging Trends in Software Metrics (WETSoM)10.1109/WETSoM.2017.8(43-48)Online publication date: May-2017
https://doi.org/10.1109/WETSoM.2017.8
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