Fuzzy logic for software metric models throughout the development life-cycle

One problem faced by managers who are using project management models is the elicitation of numerical inputs. Obtaining these with any degree of confidence early in a project is not always feasible. Related to this difficulty is the risk of precisely specified outputs from models leading to overcommitment. These problems can be seen as the collective failure of software measurements to represent the inherent uncertainties in managers' knowledge of the development products, resources, and processes. It is proposed that fuzzy logic techniques can help to overcome some of these difficulties by representing the imprecision in inputs and outputs, as well as providing a more expert-knowledge based approach to model building. The use of fuzzy logic for project management however should not be the same throughout the development life cycle. Different levels of available information and desired precision suggest that it can be used differently depending on the current phase, although a single model can be used for consistency.Unpublished[1] N. E. Fenton and S. L. Pfleeger. Software Metrics: A Rigorous & Practical Approach. PWS, 1997. [2] A. Gray and S. MacDonell. Applications of fuzzy logic to software metric models for development effort estimation. In Proceedings of the 1997 Annual meeting of the North American Fuzzy Information Processing Society - NAFIPS’97, pages 394–399. IEEE, 1997. [3] A. Gray and S. MacDonell. A comparison of model building techniques to develop predictive equations for software metrics. Information and Software Technology, 39:425–437, 1997. [4] R. Kilgour, A. Gray, P. Sallis, and S. MacDonell. A fuzzy logic approach to computer software source code authorship analysis. In Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pages 865–868. Springer-Verlag, 1997. [5] S. MacDonell and A. Gray. A comparison of modeling techniques for software development effort prediction. In Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pages 869–872. Springer-Verlag, 1997. [6] S. G. MacDonell and A. R. Gray. Fulsome: a fuzzy logic modeling tool for software metricians. In this volume. [7] S. G. MacDonell, A. R. Gray, and J. Calvert. Fulsome: A fuzzy logic toolbox for software metric practitioners and researchers. Submitted to ICONIP’99. [8] Y. Miyazaki, M. Terakado, K. Ozaki, and N. Nozaki. Robust regresison for developing software estimation models. Journal of System and Software, 27:35–16, 1994. [9] R. S. Pressman. Software Engineering: A Practitioner’s Approach. McGraw-Hill, fourth edition, 1997