A Novel Algorithmic Cost Estimation Model Based on Soft Computing Technique

Abstract

Problem statement: Software development effort estimation is the process of predicting the most realistic use of effort required for developing software based on some parameters. It has always characterized one of the biggest challenges in Computer Science for the last decades. Because time and cost estimate at the early stages of the software development are the most difficult to obtain and they are often the least accurate. Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), COCOMO II model and function points, require an estimation process in a long term. But, nowadays that is not acceptable for software developers and companies. Newer soft computing techniques to effort estimation based on non-algorithmic techniques such as Fuzzy Logic (FL) may offer an alternative for solving the problem. This work aims to propose a new fuzzy logic realistic model to achieve more accuracy in software effort estimation. The main objective of this research was to investigate the role of fuzzy logic technique in improving the effort estimation accuracy by characterizing inputs parameters using two-side Gaussian function which gave superior transition from one interval to another. Approach: The methodology adopted in this study was use of fuzzy logic approach rather than classical intervals in the COCOMO II. Using advantages of fuzzy logic such as fuzzy sets, inputs parameters can be specified by distribution of its possible values and these fuzzy sets were represented by membership functions. In this study to get a smoother transition in the membership function for input parameters, its associated linguistic values were represented by two-side Gaussian Membership Functions (2-D GMF) and rules. Results: After analyzing the results attained by means of applying COCOMO II and proposed model based on fuzzy logic to the NASA dataset and created an artificial dataset, it had been found that proposed model was performing better than ordinal COCOMO II and the achieved results were closer to the actual effort. The relative error for proposed model using two-side Gaussian membership functions is lower than that of the error obtained using ordinal COCOMO II. Conclusion: Based on the achieved results, it was concluded that, using soft computation approaches such as fuzzy logic and their advantages, good predication; adaption; understandability and the accuracy of software effort estimation can be improved and the estimation can be very close to the actual effort. This novelty model will lead researchers to focus on benefits of non-algorithmic models to overcome the estimation problems.