Improving the Accuracy of Effort Estimation through Fuzzy Set Representation of Size

Abstract

Problem statement: The precision and reliability of the effort estimation is very important for the competitiveness of software companies. The uncertainty at the input level of the Constructive Cost Model (COCOMO) yields uncertainty at the output, which leads to gross estimation error in the effort estimation. Fuzzy logic-based cost estimation models are more appropriate when vague and imprecise information was to be accounted for and was used in this research to improve the effort estimation accuracy. This study proposed to extend the COCOMO by incorporating the concept of fuzziness into the measurements of size. The main objective of this research was to investigate the role of size in improving the effort estimation accuracy by characterizing the size of the project using trapezoidal function which gave superior transition from one interval to another. Approach: The methodology adopted in this study was use of fuzzy sets rather than classical intervals in the COCOMO. Using fuzzy sets, size of a software project can be specified by distribution of its possible values and these fuzzy sets were represented by membership functions. Though, Triangular membership functions (TAMF) was used in the literature to represent the size, but it was not appropriate to clear the vagueness in the project size. Therefore, to get a smoother transition in the membership function, the size of the project, its associated linguistic values were represented by trapezoidal shaped MF and rules. Results: After analyzing the results attained by means of applying COCOMO, triangular and trapezoidal MF models to the COCOMO dataset, it had been found that proposed model was performing better than ordinal COCOMO and trapezoidal function was performing better than triangular function, as it demonstrated a smoother transition in its intervals and the achieved results were closer to the actual effort. The relative error for COCOMO using trapezoidal function is lower than that of the error obtained using TAMF. Conclusion: From the experimental results, it was concluded that, by fuzzifying the project size using TPMF, the accuracy of effort estimation can be improved and the estimated effort can be very close to the actual effort.