Dr. Sonia Juneja
@imsec.ac.in
IMS Engineering College, Ghaziabad
Scopus Publications
Scopus Publications
Sonia Chhabra and Harvir Singh
Springer Science and Business Media LLC
Sonia Chhabra and Harvir Singh
World Scientific Pub Co Pte Ltd
Estimation of software cost and effort is of prime importance in software development process. Accurate and reliable estimation plays a vital role in successful completion of the project. To estimate software cost, various techniques have been used. Constructive Cost Model (COCOMO) is amongst most prominent algorithmic model used for cost estimation. Different versions of COCOMO consider different types of parameters affecting overall cost. Parameters involved in estimation using COCOMO possess vagueness which introduces some degree of uncertainty in algorithmic modelling. The concept of fuzzy logic can deal with uncertainty involved in Intermediate COCOMO cost driver measurements via Fuzzy Inference System (FIS). In the proposed research, an effort has been made wherein, for each cost driver, an FIS is designed to calculate the corresponding effort multiplier. Proposed research provides an insight through evolutionary-based optimization techniques to optimize fuzzy logic-based COCOMO using Particle Swarm Optimization Algorithm. The magnitude of relative error and its mean, calculated using COCOMO NASA2 and COCOMONASA datasets are used as evaluation metrics to validate the proposed model. The model outperforms when compared to other optimization techniques like Genetic Algorithm.
Sonia Chhabra and Harvir Singh
IEEE
Accurate estimation of the cost of a software minimizes the risk for the software development process. Applicability of the different cost estimation models is very crucial as the information required for the implementation of such models is imprecise and vague. In order to increase the accuracy of the model, it is proposed to introduce the concept of fuzzification for calculating the value of effort multiplier corresponding to different of cost drivers used in COCOMO model. The proposed model is designed using the MATLAB and is modeled in SIMULINK. The results are validated using the COCOMO dataset and it has been observed that by fuzzifying the cost drivers the model generated the results more closer to the actual values and thus enhances the accuracy of the estimation process.