Automatic Reusability Appraisal of Software Components using Neuro-fuzzy Approach
Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.
[1] E. Smith, A. Al-Yasiri, and M. Merabti, "A Multi-Tiered Classification
Scheme For Component Retrieval," Proc. Euromicro Conference, 1998,
24th Volume 2, 25-27 Aug. 1998, pp. 882 - 889.
[2] V.R. Basili, "Software Development: A Paradigm for the Future," Proc.
COMPAC ÔÇÿ89, Los Alamitos, Calif.: IEEE CS Press, 1989, pp. 471-
485.
[3] B.W. Boehm and R. Ross, "Theory-W Software Project Management:
Principles and Examples," IEEE Trans. Software Eng., vol.15, no. 7,
1989, pp. 902.
[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. Tracz, "A Conceptual Model for Megaprogramming," SIGSOFT
Software Engineering Notes, Vol. 16, No. 3, July 1991, pp. 36-45.
[8] Stephen R. Schach and X. Yang, "Metrics for targeting candidates for
reuse: an experimental approach," ACM, SAC 1995, pp. 379-383.
[9] J. S. Poulin, Measuring Software Reuse-Principles, Practices and
Economic Models, Addison-Wesley, 1997.
[10] W. Humphrey, Managing the Software Process, SEI Series in Software
Engineering, Addison-Wesley, 1989.
[11] L. Sommerville, Software Engineering, Addision-Wesley, 4th Edition,
1992.
[12] R. S. Pressman, Software Engineering: A Practitioner-s Approach,
McGraw-Hill Publications, 5th edition, 2005.
[13] 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.
[14] S. V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic-
Basic Concepts and Applications, IEEE Press, 1996, pp. 153-160.
[15] T. Hofmann., "Probabilistic latent semantic indexing," In Proceedings of
SIGIR'99, 1999.
[16] T. MaCabe, "A Software Complexity measure," IEEE Trans. Software
Engineering, vol. SE-2, December 1976, pp. 308-320.
[17] Richard W. Selby, "Enabling Reuse-Based Software Development of
Large-Scale Systems", IEEE IEEE Trans. Software Engineering, VOL.
31, NO. 6, June 2005, pp. 495-510.
[18] Maurice H. Halstead, Elements of Software Science, Elsevier North-
Holland, New York, 1977.
[19] M. Berry, S.T. Dumais, and G.W. O'Brien, "Using Linear Algebra For
Intelligent Information Retrieval," SIAM: Review, 37(4), 1995, pp.
573-595.
[20] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer and R.
Harshman, "Indexing By Latent Semantic Analysis," Journal of the
American Society For Information Science, 41, 1990, pp. 391-407.
[21] S. T. Dumais, "LSI meets TREC: A status report," Text Retrieval
Conference, 1992, pp. 137-152.
[22] J-S. R. Jang and C.T. Sun, "Neuro-fuzzy Modeling and Control,"
Proceeding of the IEEE, March 1995.
[1] E. Smith, A. Al-Yasiri, and M. Merabti, "A Multi-Tiered Classification
Scheme For Component Retrieval," Proc. Euromicro Conference, 1998,
24th Volume 2, 25-27 Aug. 1998, pp. 882 - 889.
[2] V.R. Basili, "Software Development: A Paradigm for the Future," Proc.
COMPAC ÔÇÿ89, Los Alamitos, Calif.: IEEE CS Press, 1989, pp. 471-
485.
[3] B.W. Boehm and R. Ross, "Theory-W Software Project Management:
Principles and Examples," IEEE Trans. Software Eng., vol.15, no. 7,
1989, pp. 902.
[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. Tracz, "A Conceptual Model for Megaprogramming," SIGSOFT
Software Engineering Notes, Vol. 16, No. 3, July 1991, pp. 36-45.
[8] Stephen R. Schach and X. Yang, "Metrics for targeting candidates for
reuse: an experimental approach," ACM, SAC 1995, pp. 379-383.
[9] J. S. Poulin, Measuring Software Reuse-Principles, Practices and
Economic Models, Addison-Wesley, 1997.
[10] W. Humphrey, Managing the Software Process, SEI Series in Software
Engineering, Addison-Wesley, 1989.
[11] L. Sommerville, Software Engineering, Addision-Wesley, 4th Edition,
1992.
[12] R. S. Pressman, Software Engineering: A Practitioner-s Approach,
McGraw-Hill Publications, 5th edition, 2005.
[13] 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.
[14] S. V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic-
Basic Concepts and Applications, IEEE Press, 1996, pp. 153-160.
[15] T. Hofmann., "Probabilistic latent semantic indexing," In Proceedings of
SIGIR'99, 1999.
[16] T. MaCabe, "A Software Complexity measure," IEEE Trans. Software
Engineering, vol. SE-2, December 1976, pp. 308-320.
[17] Richard W. Selby, "Enabling Reuse-Based Software Development of
Large-Scale Systems", IEEE IEEE Trans. Software Engineering, VOL.
31, NO. 6, June 2005, pp. 495-510.
[18] Maurice H. Halstead, Elements of Software Science, Elsevier North-
Holland, New York, 1977.
[19] M. Berry, S.T. Dumais, and G.W. O'Brien, "Using Linear Algebra For
Intelligent Information Retrieval," SIAM: Review, 37(4), 1995, pp.
573-595.
[20] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer and R.
Harshman, "Indexing By Latent Semantic Analysis," Journal of the
American Society For Information Science, 41, 1990, pp. 391-407.
[21] S. T. Dumais, "LSI meets TREC: A status report," Text Retrieval
Conference, 1992, pp. 137-152.
[22] J-S. R. Jang and C.T. Sun, "Neuro-fuzzy Modeling and Control,"
Proceeding of the IEEE, March 1995.
@article{"International Journal of Information, Control and Computer Sciences:59958", author = "Parvinder S. Sandhu and Hardeep Singh", title = "Automatic Reusability Appraisal of Software Components using Neuro-fuzzy Approach", abstract = "Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.", keywords = "Clustering, ID3, LSA, Neuro-fuzzy System, SVD", volume = "1", number = "8", pages = "2539-7", }