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Identification of Reusable Software Modules in Function Oriented Software Systems using Neural Network Based Technique

Authors: Parvinder S. Sandhu, Sonia Manhas, Vinay Chopra, Nirvair Neeru


The cost of developing the software from scratch can be saved by identifying and extracting the reusable components from already developed and existing software systems or legacy systems [6]. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. We have used metric based approach for characterizing a software module. In this present work, the metrics McCabe-s Cyclometric Complexity Measure for Complexity measurement, Regularity Metric, Halstead Software Science Indicator for Volume indication, Reuse Frequency metric and Coupling Metric values of the software component are used as input attributes to the different types of Neural Network system and reusability of the software component is calculated. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Keywords: Neural Networks, Software Reusability, Accuracy, RMSE, MAE

Digital Object Identifier (DOI):

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[1] Gill, Nasib S., "Importance of Software Component Characterization for Better Software Reusability", ACM SIGSOFT Software Engineering Notes, vol. 31 No. 1, Jan 2006, pp. 1-3.
[2] Gomes, P. and Bento, C., "A Case Similarity Metric For Software Reuse And Design", Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 15, issue 1, 2001, pp. 21-35.
[3] Isakowitz, T. and Kauffman, R.J., "Supporting Search For Reusable Software Objects", IEEE Trans. Software Eng., vol. 22, issue 6, Jun 1996, pp. 407-423.
[4] W. Lim, "Effects of Reuse on Quality, Productivity, and Economics," IEEE Software, vol. 11, no. 5, Oct. 1994, pp. 23-30.
[5] H. Mili, F. Mili and A. Mili, "Reusing Software: Issues And Research Directions," IEEE Transactions on Software Engineering, Volume 21, Issue 6, June 1995, pp. 528 - 562.
[6] G. Caldiera and V. R. Basili, "Identifying and Qualifying Reusable Software Components", IEEE Computer, February 1991, pp. 61-70.
[7] W. Humphrey, Managing the Software Process, SEI Series in Software Engineering, Addison-Wesley, 1989.
[8] L. Sommerville, Software Engineering, Addision-Wesley, 4th Edition, 1992.
[9] R. S. Pressman, Software Engineering: A Practitioner-s Approach, McGraw-Hill Publications, 5th edition, 2005.
[10] G. Boetticher and D. Eichmann, "A Neural Network Paradigm for Characterising Reusable Software," Proceedings of the 1st Australian Conference on Software Metrics, 18-19 November 1993.
[11] Parvinder Singh Sandhu and Hardeep Singh, "Automatic Reusability Appraisal of Software Components using Neuro-Fuzzy Approach", International Journal Of Information Technology, vol. 3, no. 3, 2006, pp. 209-214..
[12] T. MaCabe, "A Software Complexity measure", IEEE Trans. Software Eng., vol. SE-2 (December 1976), pp. 308-320.
[13] G. Caldiera and V. R. Basili, Identifying and Qualifying Reusable Software Components, IEEE Computer, (1991), pp. 61-70.
[14] Herenji, H. R. and Khedkar, P (1992), "Learning and Tuning Fuzzy Logic Controllers through Reinforcements", IEEE Transactions on Neural Networks, vol. 3, 1992, pp. 724-740.
[15] Challagulla, V.U.B., Bastani, F.B., I-Ling Yen, Paul, (2005), "Empirical assessment of machine learning based software defect prediction techniques", 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems, WORDS 2005, 2-4 Feb 2005, pp. 263- 270.