research-article

Empirical study of Software Quality estimation

Authors:

Inderpreet Kaur,

Arvinder KaurAuthors Info & Claims

CCSEIT '12: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology

Pages 694 - 700

https://doi.org/10.1145/2393216.2393332

Published: 26 October 2012 Publication History

Abstract

Software Quality is an important nonfunctional requirement which is not satisfied by many software products. Prediction models using object oriented metrics can be used to identify the faulty classes. In this paper, we will empirically study the relationship between object oriented metrics and fault proneness of an open source project Emma. Twelve machine Learning classifiers have been used. Univariate and Multivariate analysis of Emma shows that Random Forest provides optimum values for accuracy, precision, sensitivity and specificity.

References

[1]

Aggarwal, K. K., Singh, Y, Kaur, A. and Malhotra, R. 2006. Empirical Study of Object-Oriented Metrics, Journal of Object Technology, 5, 8.

[2]

AHA, D. W. and Daniel, J. J. 1991. Instance-Based Learning Algorithms, Kluwer Academic Publishers, Boston. Manufactured in The Netherlands, Machine Learning, 6, 37--66.

Digital Library

[3]

Alshayeb M. and Li, W. 2003. An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes. IEEE transaction on software engineering, 12, 11, 1043--1049.

Digital Library

[4]

Basili, V., Briand L. and Melo, W. L. 1996. A Validation of Object-Oriented Design Metrics as Quality Indicators. IEEE Transactions on Software Engineering, 22, 10, 267--271.

Digital Library

[5]

Bellini, P., Bruno, I., Nesiand P. and Rogai, D. 2005. Comparing fault-proneness estimation models", in Proc. of 10th IEEE International Conference on Engineering of Complex Computer Systems, 205--214.

Digital Library

[6]

Breiman L. 2001. Random Forests. Machine Learning, 45, 1, 5--32.

Digital Library

[7]

Breiman, L. 1996. Bagging Predictors. Machine Learning, 26, 123--140

Digital Library

[8]

Briand, L. and Wust, J. 2001. Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs, Empirical Software Engineering: An International Journal, 6(1), 11--58.

Digital Library

[9]

Briand, L., Daly, J. and Wust, J. 1999. A Unified Framework for Coupling Measurement in Object-Oriented Systems, IEEE Transactions on software Engineering, 25, 91--121.

Digital Library

[10]

Briand, L., Daly, J., Porter, V. and Wust, J. 2000. Exploring the relationships between design measures and software quality, Journal of Systems and Software, 5, 245--273.

Digital Library

[11]

Briand, L., Melo, W. L. and Wust J. 2002. Assessing the applicability of fault-proneness models across object oriented software projects, IEEE Trans. on Software Engineering, vol. 28--7, 706--720.

Digital Library

[12]

Chidamber, S. and Kemerer, C. F. 1994. A metrics Suite for Object-Oriented Design. IEEE Transactions on Software Engineering., SE-20, 6, 476--493.

Digital Library

[13]

Chidamber, S. R. and Kemerer, C. F. 1991. Towards a metrics suite for object oriented design", in Proceedings of 6th ACM Conference on Object-Oriented Programming Systems Languagesand Applications (OOPSLA), Phoenix, Arizona, 197--211.

Digital Library

[14]

Chidamber, S., Darcy, D. and Kemerer, C. 1998. Managerial use of Metrics for Object-Oriented Software: An Exploratory Analysis. IEEE Transactions on Software Engineering, 24, 8, 629--639. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68--73.

Digital Library

[15]

CKJM available at http://gromit.iiar.pwr.wroc.pl/p_inf/ckjm/

[16]

Cristianini, N. and Taylor, J. S. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge, UK.

Digital Library

[17]

Emam, K. El, Benlarbi, S., Goel, N. and Rai, S. 2001. The Confounding Effect of Class Size on The Validity of Object-Oriented Metrics. IEEE Transactions on Software Engineering, 27, 7, 630--650.

Digital Library

[18]

Emam, K. El, Melo, W. and Machado, J. 2001. The Prediction of Faulty Classes Using Object-Oriented Design Metrics, Journal of Systems and Software, 56, 63--75.

Digital Library

[19]

Emma. http://sourceforge.net/projects/emma

[20]

Gyimothy, T., Ferenc, R. and Siket, I. 2005. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Trans. Software Engineering, 31, 10, 897--910.

Digital Library

[21]

Henderson-sellers, B. 1996. Object-Oriented Metrics, Measures of Complexity, Prentice Hall, 1996 ISBN: 0-13-239872-9.

Digital Library

[22]

Hitz, M. and Montazeri, B. 1995. Measuring Coupling and Cohesion in Object-Oriented Systems. Proc. Int. Symposium on Applied Corporate Computing, Monterrey, Mexico.

[23]

Hodge, V. J., Jackson, T. and Austin, J. 2010. A Binary Neural Decision Table Classifier. Advanced Computer Architecture Group Department of Computer Science University of York, York, YO10 5DD, UK.

[24]

Hosmer, D. W. and Lemeshow, S. Applied Logistic Regression ISBN: 9780471356325.

[25]

Huang, K. 2003. Discriminative Naive Bayesian Classifiers, Department of Computer Science and Engineering, the Chinese University of Hong Kong.

[26]

Khoshgoftaar, T. M. and Seliya, N. 2004. Comparative assessment of software quality classification techniques: An empirical study. Empirical Software Engineering, 9, 229--257.

Digital Library

[27]

Koru, A. and Liu, H. 2005. Building effective defect prediction models in practice, IEEE Software,. 23--29

Digital Library

[28]

Leonardo N. Multilayer perceptron tutorial available at http://www.cs.sun.ac.za/~kroon/courses/machine_learning/lecture5/mlp.pdf

[29]

Li, W. and Henry, S. 1993. Object Oriented Metrics that Predict Maintainability. Journal of Systems and Software, 23, 2, 111--122.

Digital Library

[30]

Lorenz, M. and Kidd, J. 1994. Object-Oriented Software Metrics. Prentice-Hall.

Digital Library

[31]

McCabe & Associates, McCabe Object Oriented Tool User's Instructions, 1994.

[32]

Menzies, T., Greenwald, J. and Frank, A. 2007. Data mining static code attributes to learn defect predictors. IEEE Trans. on Software Engineering, 33, 1, 2--13.

Digital Library

[33]

Mitchell, T. 1997 Machine Learning, ISBN 0070428077, McGraw Hill, 1997, available at http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html

Digital Library

[34]

Olague, H., Etzkorn, L., Gholston, S. and Quat-tlebaum S. 2007 Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes. IEEE Transactions on software Engineering, 33, 8, 402--419.

Digital Library

[35]

Rosenberg, L. and Hyatt, L. 1995. Software Quality Metrics for Object Oriented System Environments, NASA Technical Report.

[36]

Schroeder, M. 1999. A practical Guide to Object-Oriented Metrics, IT Professional, 1--6, 30--36.

Digital Library

[37]

Stone, M. 1974. Cross-validatory choice and assessment of statistical predictions, J. Royal Stat. Soc., 36, 111--147.

[38]

Subramanyam, R. and Krishnan, M. S. 2003. Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects. IEEE transaction on software engineering, 29, 4, 297--310.

Digital Library

[39]

Yan, L. Bayesian Classification available at http://www.cecs.wright.edu/~yan.liu/IHE631/Color.pdf

Cited By

Lumbard KGermonprez MGoggins S(2023)An empirical investigation of social comparison and open source community healthInformation Systems Journal10.1111/isj.1248534:2(499-532)Online publication date: 15-Nov-2023
https://doi.org/10.1111/isj.12485
Mateus BMartinez MKolski C(2023)Learning migration models for supporting incremental language migrations of software applicationsInformation and Software Technology10.1016/j.infsof.2022.107082153:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.infsof.2022.107082
Kaur GKaur HGoyal S(2022)Correlation analysis between different parameters to predict cement logisticsInnovations in Systems and Software Engineering10.1007/s11334-022-00505-yOnline publication date: 2-Dec-2022
https://doi.org/10.1007/s11334-022-00505-y
Show More Cited By

Index Terms

Empirical study of Software Quality estimation
1. Computing methodologies
  1. Machine learning
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
        Information science education

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cover image ACM Other conferences

CCSEIT '12: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology

October 2012

800 pages

ISBN:9781450313100

DOI:10.1145/2393216

General Chairs:
Natarajan Meghanathan
Jackson State University
,
Michal Wozniak
Wroclaw University of Technology, Poland

Copyright © 2012 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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Avinashilingam University: Avinashilingam University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2012

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CCSEIT '12

Sponsor:

Avinashilingam University

CCSEIT '12: The Second International Conference on Computational Science, Engineering

October 26 - 28, 2012

Coimbatore UNK, India

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Cited By

Lumbard KGermonprez MGoggins S(2023)An empirical investigation of social comparison and open source community healthInformation Systems Journal10.1111/isj.1248534:2(499-532)Online publication date: 15-Nov-2023
https://doi.org/10.1111/isj.12485
Mateus BMartinez MKolski C(2023)Learning migration models for supporting incremental language migrations of software applicationsInformation and Software Technology10.1016/j.infsof.2022.107082153:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.infsof.2022.107082
Kaur GKaur HGoyal S(2022)Correlation analysis between different parameters to predict cement logisticsInnovations in Systems and Software Engineering10.1007/s11334-022-00505-yOnline publication date: 2-Dec-2022
https://doi.org/10.1007/s11334-022-00505-y
Goggins SLumbard KGermonprez M(2021)Open Source Community Health: Analytical Metrics and Their Corresponding Narratives2021 IEEE/ACM 4th International Workshop on Software Health in Projects, Ecosystems and Communities (SoHeal)10.1109/SoHeal52568.2021.00010(25-33)Online publication date: May-2021
https://doi.org/10.1109/SoHeal52568.2021.00010
Shaikh SChangan LMalik MKhan M(2019)Software Defect-Prone Classification using Machine Learning: A Virtual Classification Study between LibSVM & LibLinear2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS48846.2019.9024799(1-6)Online publication date: Dec-2019
https://doi.org/10.1109/MACS48846.2019.9024799

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