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Transactions on Engineering Technologies pp 161–175Cite as

A Metric Suite for Predicting Software Maintainability in Data Intensive Applications

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Abstract

Software maintainability is the vital aspect of software quality and defined as the ease with which modifications can be made once the software is delivered. Tracking the maintenance behaviour of a software product is very complex that is widely acknowledged by the researchers. Many research studies have empirically validated that the prediction of object oriented software maintainability can be achieved before actual operation of the software using design metrics proposed by Chidamber and Kemerer (C&K). However, the framework and reference architecture in which the software systems are being currently developed have changed dramatically in recent times due to the emergence of data warehouse and data mining field. In the prevailing scenario, certain deficiencies were discovered when C&K metric suite was evaluated for data intensive applications. In this study, we propose a new metric suite to overcome these deficiencies and redefine the relationship between design metrics with maintainability. The proposed metric suite is evaluated, analyzed and empirically validated using five proprietary software systems. The results show that the proposed metric suite is very effective for maintainability prediction of all software systems in general and for data intensive software systems in particular. The proposed metric suite may be significantly helpful to the developers in analyzing the maintainability of data intensive software systems before deploying them.

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References

  1. IEEE Standard 1219–1993. IEEE Standard for Software Maintenance. INSPEC Accession Number: 4493167 doi: 10.1109/IEEESTD.1993.115570 June, 1993

  2. Software Engineering Standards Committee of the IEEE Computer Society, IEEE Std. 828–1998 IEEE Standard for Software Configuration Management Plans, http://standards.ieee.org/findstds/standard/828-1998.html

  3. K.K. Aggarwal, Y. Singh, A. Kaur, R. Malhotra, Analysis of object-oriented metrics, in International Workshop on Software Measurement (IWSM), 2005

    Google Scholar 

  4. R. Bandi, Predicting maintenance performance using object-oriented design complexity metrics. IEEE Trans. Softw. Eng. 29(1), 77–87 (2003)

    Google Scholar 

  5. S. Chidamber, C. Kemerer, A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476–493 (1994)

    Article  Google Scholar 

  6. W. Li, S. Henry, Object-oriented metrics that predict maintainability. J. Syst. Softw. 23, 111–122 (1993)

    Article  Google Scholar 

  7. K.K. Aggarwal, Y. Singh, A. Kaur, R. Malhotra, Application of artificial neural network for predicting maintainability using object oriented metrics. Proc. World Acad. Sci. Eng. Technol. 15, 285–289 (2006)

    Google Scholar 

  8. Y. Zhou, H. Leung, Predicting object-oriented software maintainability using multivariate adaptive regression splines. J. Syst. Softw. 80(8), 1349–1361 (2007)

    Google Scholar 

  9. M. Dagpinar, J.H. Jahnke, Predicting maintainability with object-oriented metrics: an empirical comparison, in Proceedings of the 10th Working Conference on Reverse Engineering (WCRE ‘03), IEEE Computer Society, Washington, 2003

    Google Scholar 

  10. M. Thwin, T. Quah, Application of neural networks for software quality prediction using object oriented metrics. J. Syst. Softw. 76(2), 147–156 (2005)

    Google Scholar 

  11. C.V. Koten, A.R. Gray, An application of Bayesian network for predicting object-oriented software maintainability. Inf. Softw. Technol. 48(1), 59–67 (2006)

    Google Scholar 

  12. M.O. Elish, K.O. Elish, Application of TreeNet in predicting object-oriented software maintainability: a comparative study, in Proceedings of European Conference on Software Maintenance and Reengineering, 2009

    Google Scholar 

  13. C. Jin, J.A. Liu, Applications of support vector machine and unsupervised learning for predicting maintainability using object-oriented metrics. in Proceedings of the 2nd International Conference on Multi Media and Information Technology, 2010

    Google Scholar 

  14. A. Kaur, K. Kaur, R. Malhotra, Soft computing approaches for prediction of software maintenance effort. Int. J. Comput. Appl. 1(16), 975–988

    Google Scholar 

  15. R. Malhotra, A. Chug, Software maintainability prediction using machine learning algorithms. Softw. Eng. Int. J. 2(2), 19–36 (2012)

    Google Scholar 

  16. W. Li, Another metric suite for object-oriented programming. J. Syst. Softw. 44(2), 155–162 (1998). doi:10.1016/O164-1212(98)10052-3

    Article  Google Scholar 

  17. P. Oman, J. Hagemeister, Metrics for assessing a software system’s maintainability, conference on software maintenance (IEEE Computer Society Press, Los Alamitos, 1992), pp. 337–344

    Google Scholar 

  18. P. Oman, J. Hagemeister, Construction and testing of polynomials predicting software maintainability. J. Syst. Softw. 24, 251–266 (1994)

    Article  Google Scholar 

  19. T. McCabe, A complexity measure. IEEE Trans. Softw. Eng. SE-2(4), 308–320 (1976)

    Google Scholar 

  20. M. Jorgensen, Experience with the accuracy of software maintenance task effort prediction models, IEEE Transc. Softw. Eng. 21(8), 674–681 (1995)

    Google Scholar 

  21. S. Muthanna, K. Kontogiannis, B. Ponnambalam, A. Stacey, Maintainability model for industrial software system using design level metrics, in Proceedings of 7th Working Conference on Reverse Engineering, 2000, pp. 248–256

    Google Scholar 

  22. F. Fioravanti, P. Nesi, Estimation and prediction metrics for adaptive maintenance effort of object-oriented system. IEEE Trans. Softw. Eng. 27(12), 1062–84 (2001)

    Google Scholar 

  23. S.C. Misra, Modeling design/coding factors that drive maintainability of software systems. Softw. Qual. J. 13(3), 297–320 (2005)

    Article  Google Scholar 

  24. A.D. Banker, A.B. Sultan, H. Zulzalil, J. Din, Applying evolution programming search based software engineering (SBSE), in Proceedings of Selecting the Best Open Source Maintainability Metrics, International Symposium, ISCAIE, 2012

    Google Scholar 

  25. P. Sun, A. Wang, Application of ant colony optimization in preventive software maintenance policy, in Proceedings of IEEE international Conference on Information Science and Technology, China, Mar 2012

    Google Scholar 

  26. R. Vivanco, N. Pizzi, Finding effective software metrics to classify maintainability using a parallel genetic algorithm. Genetic Evol. Comput. (Lecture Notes in Computer Science) 30(13), 1388–1399 (2004)

    Google Scholar 

  27. R. Malhotra, A. Chug, An empirical study to redefine the relationship between software design metrics and maintainability in high data intensive applications, in Proceedings of the World Congress on Engineering and Computer Science 2013, WCECS 2013. Lecture Notes in Engineering and Computer Science, San Francisco, 23–25 Oct, 2013, pp. 61–66

    Google Scholar 

  28. C and C++ Code Counter to Compute OO Metrics and the McCabe Cyclomatic Complexity Metrics from Source Code. http://cccc.sourceforge.net/

  29. R. Malhotra, A. Chug, An empirical validation of group method of data handling on software maintainability prediction using object oriented systems, in Proceedings of International Conference on Quality Reliability InfoCom Technology and Industrial Technology, ICQRITM 2012, New Delhi, pp. 49–57

    Google Scholar 

  30. M.C.J. Hu, Application of the adaline system to weather forecasting. Master thesis, technical report 6775-i, Stanford Electronics Laboratories, 1964

    Google Scholar 

  31. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning Internal Presentation by Back-Propagating Errors, the PDP Research Group, Parallel Distributing Processing: Exploration in the Microstructure of Cognition (MIT Press, MA, 1994)

    Google Scholar 

  32. T. Kohonen, Self-Organization and Associative Memory (Springer, Berlin, 1989)

    Book  Google Scholar 

  33. A.E. Bryson, Y.C. Ho, Applied Optimal Control: Optimization, Estimation, and Control (Blaisdell Publishing Company, New York, 1969), p. 481

    Google Scholar 

  34. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, 2nd edn. (Prentice Hall, Upper Saddle River, 2003)

    Google Scholar 

  35. D.F. Specht, A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)

    Article  Google Scholar 

  36. B.A. Kitchenham, L.M. Pickard, S.G. MacDonell, M.J. Shepperd, What accuracy statistics really measure. IEE Proc. Softw. 148(3), 81–85 (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Software Engineering, Delhi Technological University, Delhi, India

    Ruchika Malhotra

  2. University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

    Anuradha Chug

Authors
  1. Ruchika Malhotra
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  2. Anuradha Chug
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Corresponding author

Correspondence to Anuradha Chug .

Editor information

Editors and Affiliations

  1. Computer & Communication, Catholic University of DaeGu, DaeGu, Korea, Republic of (South Korea)

    Haeng Kon Kim

  2. IAENG Secretariat, International Association of Engineers, Hong Kong, Hong Kong SAR

    Sio-Iong Ao

  3. College of Engineering, California State Polytechnic University, Pomona, California, USA

    Mahyar A. Amouzegar

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© 2014 Springer Science+Business Media Dordrecht

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Malhotra, R., Chug, A. (2014). A Metric Suite for Predicting Software Maintainability in Data Intensive Applications. In: Kim, H., Ao, SI., Amouzegar, M. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9115-1_13

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  • DOI: https://doi.org/10.1007/978-94-017-9115-1_13

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-9114-4

  • Online ISBN: 978-94-017-9115-1

  • eBook Packages: EngineeringEngineering (R0)

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