Applications of fuzzy logic to software metric models for development effort estimation

Software metrics are measurements of the software development process and product that can be used as variables (both dependent and independent) in models for project management. The most common types of these models are those used for predicting the development effort for a software system based on size, complexity, developer characteristics, and other metrics. Despite the financial benefits from developing accurate and usable models, there are a number of problems that have not been overcome using the traditional techniques of formal and linear regression models. These include the non-linearities and interactions inherent in complex real-world development processes, the lack of stationarity in such processes, over-commitment to precisely specified values, the small quantities of data often available, and the inability to use whatever knowledge is available where exact numerical values are unknown. The use of alternative techniques, especially fuzzy logic, is investigated and some usage recommendations are made.Unpublished[1] N. Fenton. Software Metrics, a Rigorous Approach. Chapman & Hall, London, 1991. [2] T. Mukhopadhyay and S. Kekre. Software effort models for early estimation of process control applications. IEEE Transactions on Software Engineering, 18(10):915-924, 1992. [3] A.L. Lederer, R. Mirani, B.S. Neo, C. Pollard, J. Prasad, and K. Ramamurthy. Information system cost estimating: a management perspective. MIS Quarterly, 159-176, June, 1990. [4] B. Londeix. Deploying realistic estimation (field situation analysis). Information and Software Technology, 37:665-670, 1995. [5] S. Kumar, B.A. Krishna, and P.S. Satsangi. Fuzzy systems and neural networks in software engineering project management. Journal of Applied Intelligence, 4:31-52, 1994. [6] R.W. Selby and A.A. Porter. Learning from examples: generation and evaluation of decision trees for software resource analysis. IEEE Transactions on Software Engineering, 14:1743-1757, 1988. [7] K. Srinivasan and D. Fisher. Machine learning approaches to estimating software development effort, IEEE Transactions on Software Engineering, 21:126-137, 1995. [8] T. Mukhopadhyay, S.S. Vicinanza, and M.J. Prietula. Examining the feasibility of a case-based reasoning model for software effort estimation. MIS Quarterly, 16:155-171, 1992. [9] A.R. Gray, and S.G. MacDonell. A comparison of model building techniques to develop predictive equations for software metrics. Information and Software Technology, to appear, 1997. [10] J.-M. Desharnais. Analyse statistique de la productivitie des projects de development en informatique apartir de la technique des points des fontion. Master’s Thesis, Universite du Montreal, 1989. [11] S.G. MacDonell and A.R. Gray. Alternatives to regression models for estimating software projects. In Proceedings of the IFPUG Fall Conference, Dallas TX, IFPUG 279.1-279.15, 1996. [12] A.J. Albrecht. Measuring application development productivity. In Proceedings of the IBM Applications Development Joint SHARE/GUIDE Symposium, Monterey, CA, 83-92, 1979. [13] J.E. Matson, B.E. Barrett, and J.M. Mellichamp. Software development cost estimation using function points. IEEE Transactions on Software Engineering, 20(4):275-287, 1994. [14] J.J. Dolado. A study of the relationships among Albrecht and Mark II function points, lines of code 4GL and effort. Journal of Systems and Software, 37:161-173, 1997. [15] R.I. Kilgour, A.R. Gray, P.J. Sallis, and S.G. MacDonell. A fuzzy logic approach to computer software source code authorship analysis. Submitted to The Fourth International Conference on Neural Information Processing -- The Annual Conference of the Asian Pacific Neural Network Assembly (ICONIP'97). [16] Y .I. Liou. Knowledge acquisition: issues, techniques and methodology. DATABASE, 59-64, Winter 1992