Computational intelligence as an emerging paradigm of software engineering

ABSTRACT

Software Engineering is inherently knowledge intensive. Software processes and products are human centered. The technology of Computational Intelligence (CI) intensively exploits various mechanisms of interaction with humans and processes domain knowledge with intent of building intelligent systems. As commonly perceived, CI dwells on three highly synergistic technologies of neural networks, fuzzy sets (or granular computing, in general) and evolutionary optimization. As the software complexity grows and the diversity of software systems skyrocket, it becomes apparent that there is a genuine need for a solid, efficient, designer-oriented vehicle to support software analysis, design, and implementation at various levels. The research agenda makes CI a highly compatible and appealing vehicle to address the needs of knowledge rich environment of Software Engineering. The objective of this study is to identify and discuss synergistic links emerging between Software Engineering and Computational Intelligence. We show how CI --- based models contribute to the methodology of constructing models of software processes and products. Several selected examples (including software cost estimation, quality, and software measures) are included.

REFERENCES

• 1: Aksit, M., Designing Software Architectures, Post-Academic Organization, the Netherlands, 2000.
• 2: Aksit, M. and Marcelloni, F., Deferring elimination of design alternatives in object-oriented methods, Concurrency --- Practice and Experience, to appear
• 3: Alvarez, J., Castell, N. and Slavkova, O., Combining knowledge and metrics to control software quality factors, Proc. 3rd Int. Conf. On Achieving Quality in Software, Florence, Italy, 1996, 201-212.
• 4: Ch. Anderson , A. von Mayrhauser , T. Chen, Assessing Neural Networks as Guides for Testing Activities, Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results, p.155, March 25-26, 1996
• 5: Back, T., Fogel, D. B. and Michalewicz, Z., Evolutionary Computation, vol. 1, 2, Institute of Physics Publ., Bristol, 2000.
• 6: Baisch, E., Bleile, T. and Belschner, R., A neural fuzzy system to evaluate software development productivity, Proc 1995 IEEE Int. Conf. On Systems Man and Cybernetics, Vancouver, Canada, 1995, vol. V, 4603-4608.
• 7: Barry W. Boehm, A Spiral Model of Software Development and Enhancement, Computer, v.21 n.5, p.61-72, May 1988 [doi>10.1109/2.59]
• 8: Boetticher, G. D., An assessment of metric contribution in the construction of a neural network-based effort estimator, Proc. 2nd Int. Workshop on Soft Computing Applied to Software Engineering, CASE 2001, February 8-9, 2001, Enschede, The Netherlands.
• 9: Lionel C. Briand , Sandro Morasca , Victor R. Basili, Property-Based Software Engineering Measurement, IEEE Transactions on Software Engineering, v.22 n.1, p.68-86, January 1996 [doi>10.1109/32.481535]
• 10: S. Cimpan, S. and Oquendo, F. Dealing with software process deviations using fuzzy logic based monitoring, ACM Software Review, 2001.
• 11: S. R. Chidamber , C. F. Kemerer, A Metrics Suite for Object Oriented Design, IEEE Transactions on Software Engineering, v.20 n.6, p.476-493, June 1994 [doi>10.1109/32.295895]
• 12: Chen, S. M., Evaluating the rate of aggregative risk in software development using fuzzy set theory. Int. J. Cybernetic Systems, 30, 1999, 245-250.
• 13: Shyi-Ming Chen, Fuzzy group decision making for evaluating the rate if aggregative risk in software development, Fuzzy Sets and Systems, v.118 n.1, p.75-88, Feb. 16, 2001 [doi>10.1016/S0165-0114(99)00103-7]
• 14: Ebert, C., An approach to fuzzy data analysis for software quality control, Proc 1st European Congress on Fuzzy and Intelligent Technologies EUFIT-95, Aachen, Germany, 1993, 1156-1161.
• 15: Christof Ebert, Rule-based fuzzy classification for software quality control, Fuzzy Sets and Systems, v.63 n.3, p.349-358, May 10, 1994 [doi>10.1016/0165-0114(94)90221-6]
• 16: Norman E. Fenton, Software Metrics: A Rigorous Approach, Chapman & Hall, Ltd., London, UK, 1991
• 17: Norman Fenton , Shari Lawrence Pfleeger, Software metrics (2nd ed.): a rigorous and practical approach, PWS Publishing Co., Boston, MA, 1997
• 18: David Garmus , David Herron, Measuring the software process: a practical guide to functional measurements, Prentice-Hall, Inc., Upper Saddle River, NJ, 1996
• 19: Hakkarainen, J., Laamanen, P. and Rask, R., Neural networks in software cost estimation, Proc 26th Hawaii Int. Conf. On System Sciences, Wailea, HI, 1993, vol. IV, 626-634.
• 20: Elaine M. Hall, Managing risk: methods for software systems development, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1998
• 21: Harman, M. and Jones, B. F., Search-based software engineering, Information and Software Technology, 43, 2001, 833-839.
• 22: Hodgkinson, A. C. and Garratt, P. W., A neurofuzzy cost estimator, Proc. 3rd Int Conf Software Engineering and Applications (SAE), 1999, 401-406.
• 23: Jahnke, J. H., Cognitive support in software reengineering based on generic fuzzy reasoning nets, Fuzzy Sets and Systems, submitted
• 24: Jones, B. F., Shamer, H. and Eyres, D. E., Automatic structural testing using genetic algorithms, Software Engineering Journal, May 1995, vol. 12, no.5.
• 25: Nachimuthu Karunanithi , Darrell Whitley , Yashwant K. Malaiya, Prediction of Software Reliability Using Connectionist Models, IEEE Transactions on Software Engineering, v.18 n.7, p.563-574, July 1992 [doi>10.1109/32.148475]
• 26: Khoshgoftaar, T. M., Pandya, A. S. and Lanning, D. L., Application of neural networks for predicting program faults, Annals of Software Engineering, 1, 1995, 141-154.
• 27: Kohonen, T., Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43, 59-69 (1982).
• 28: Teuvo Kohonen, Self-organizing maps, Springer-Verlag New York, Inc., Secaucus, NJ, 1997
• 29: Anita Lee , Chun Hung Cheng , Jaydeep Balakrishnan, Software development cost estimation integrating neural network with cluster analysis, Information and Management, v.34 n.1, p.1-9, Aug. 5, 1998 [doi>10.1016/S0378-7206(98)00041-X]
• 30: Raymond J. Madachy, Heuristic Risk Assessment Using Cost Factors, IEEE Software, v.14 n.3, p.51-59, May 1997 [doi>10.1109/52.589234]
• 31: MacDonell, S. G., Gray, A. R. and Calvert J. M., FULSOME: A fuzzy logic modeling tool for software metricians. In: Proc 18th Int Conference NAFIPS-99, N. York, June 10-12, 1999.
• 32: Marcelloni, F. and Aksit, M., Reducing quantization error and contextual bias problems in software development processes by applying fuzzy logic. In: Proc 18th Int Conference NAFIPS-99, N. York, June 10-12, 1999, 268-272.
• 33: Marcelloni, F. and Aksit, M., Improving object-oriented methods by using fuzzy logic, ACM Software Review, 2001.
• 34: McCall, J. A., Richards, P. K. and Walters, G. F., Factors in Software Quality, vols. I, II, III. U.S. Rome Air Development Center Reports, NTIS AD/A- 049 014, 015, 055, 1977.
• 35: McCall J. A. et al.: Software Quality Measurement Manual. RADC Technical Report TR-80-109, U.S. Rome Air Development Center, 1980.
• 36: McCall, J. A., Quality factors. In: Encyclopedia of Software Engineering, Marciniak, J. J. (Ed.), New York, John Wiley & Sons, 2 (1994) 959-969.
• 37: Merkl, D. A connectionist view of document classification, In: Proc. of the 6th Australasian Database Conference, Adelaide, SA, 1995.
• 38: Merkl, D., Self-organizing maps and software reuse, In {42}, 65-95.
• 39: C. C. Michael , G. McGraw , M. A. Schatz, Generating Software Test Data by Evolution, IEEE Transactions on Software Engineering, v.27 n.12, p.1085-1110, December 2001 [doi>10.1109/32.988709]
• 40: Paul, R., Metric-based neural network classification tool for analyzing large-scale software, Proc. 4th Int Conf. On Tools with Artificial Intelligence, Arlington, VA, Nov. 1992, 108-113.
• 41: Witold Pedrycz, Computational Intelligence: An Introduction, CRC Press, Inc., Boca Raton, FL, 1997
• 42: Witold Pedrycz , W. Pedrycz , J. F. Peters, Computational Intelligence in Software Engineering, World Scientific Publishing Co., Inc., River Edge, NJ, 1998
• 43: Pedrycz, W., Peters, J. F. and Ramanna, S., Neuro-fuzzy approach to software quality, Proc. Annual Oregon Workshop on Software Metrics, Idado, May 1997, 1-9.
• 44: Pedrycz, W., Peters, J. F. and Ramanna, S., Design of a software quality decision system: A computational intelligence approach. Proc. of the IEEE Canadian Conf. on Electrical & Computer Engineering (CCECE'98), Waterloo, Ontario, 25-28 May 1998, 513-516.
• 45: Witold Pedrycz , James Waletzky, Fuzzy clustering in software reusability, Software—Practice & Experience, v.27 n.3, p.245-270, March 1997 [doi>10.1002/(SICI)1097-024X(199703)27:3<245::AID-SPE83>3.3.CO;2-S]
• 46: Pedrycz, W. and Gomide, F., An Introduction to Fuzzy Sets: Analysis and Design, MIT Press, Cambridge, MA, 1998.
• 47: Witold Pedrycz, Granular computing: an emerging paradigm, Physica-Verlag GmbH, Heidelberg, Germany, 2001
• 48: Peters J. F. and Ramanna S.,: Application of Choquet Integral in Software Cost Estimation. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE'96), New Orleans, 1996, 862-866.
• 49: Reynolds, R. G., Zannoni, E. and Posner, R. M., Learning to understand software using cultural algorithms, Proc 3rd Annual Conf. On Evolutionary Programming, San Diego, CA, Feb. 1994, 150-157.
• 50: Roper, M., Computer aided software testing using genetic algorithms. Software Research Institute 9T1, 1997.
• 51: Saaty, T. L., The Analytic Hierarchy Process, McGraw Hill, N. York, 1980.
• 52: Samson, B., Ellison, D. and Dugard, P., Software cost estimation using an Albus perceptron, Information and Software Technology, 1997, 55-60.
• 53: Norman F. Schneidewind, Methodology for Validating Software Metrics, IEEE Transactions on Software Engineering, v.18 n.5, p.410-422, May 1992 [doi>10.1109/32.135774]
• 54: Martin Shepperd , Michelle Cartwright, Predicting with Sparse Data, IEEE Transactions on Software Engineering, v.27 n.11, p.987-998, November 2001 [doi>10.1109/32.965339]
• 55: Martin Shepperd , Michelle Cartwright , Gada Kadoda, On Building Prediction Systems for Software Engineers, Empirical Software Engineering, v.5 n.3, p.175-182, November 2000 [doi>10.1023/A:1026582314146]
• 56: Miyoung Shin , Amrit L. Goel, Empirical Data Modeling in Software Engineering Using Radial Basis Functions, IEEE Transactions on Software Engineering, v.26 n.6, p.567-576, June 2000 [doi>10.1109/32.852743]
• 57: von Mayrhauser, A., Anderson, C.. and R. Mraz, R., Using a neural network to predict test case effectiveness, Proc. IEEE Aerospace Applications Conf. Snowmass, CO, Feb 1995.
• 58: Watkins, A. L., The automatic generation of test data using genetic algorithms, Proc 4th Software Quality Conf. Dundee, UK, July 1995, vol. II, 300-309.
• 59: Wittig, G. and Finnie, G., Estimating software development effort with connectionist models, Information & Software Technology, 39, 1997, 469-476.
• 60: Zadeh, L. A., Fuzzy sets and information granularity, In: M. M. Gupta, R. K. Ragade, R. R. Yager, eds., Advances in Fuzzy Set Theory and Applications, North Holland, Amsterdam, 3-18 (1979).
• 61: Zadeh, L. A., Fuzzy logic = Computing with words, IEEE Trans. on Fuzzy Systems, 4, 103-111 (1996).
• 62: Zadeh, L. A., Fuzzy logic = Computing with words, IEEE Trans. on Fuzzy Systems, vol. 4, 2, 1996, 103-111.
• 63: Lotfi A. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, v.90 n.2, p.111-127, Sept. 1, 1997 [doi>10.1016/S0165-0114(97)00077-8]
• 64: Horst Zuse, A Framework of Software Measurement, Walter de Gruyter & Co., Hawthorne, NJ, 1997

INDEX TERMS

General Terms:
Algorithms, Design, Measurement, Performance

Keywords:
computational intelligence, data visualization, genetic optimization, granular computing, neural networks, software quality, synergy, uncertainty representation