Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm

Abstract

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.

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

“…In [55] in order to optimize the fuzzy model for software cost estimation, the PSO is used to optimize the parameter values of model membership functions. Kaushik et al [57] uses ANN and whale optimization algorithm (WOA) to provide an effort estimation method for agile software development.…”

“…To illustrate the generality of the proposed approach, the results are computed using three software estimation datasets: (i) NASA [30] (ii) COCOMO-81 [31], and (iii) China dataset [55]. The aim is to find out whether the accuracy is dataset dependent.…”

“…The effort attribute is considered as the dependent entity. China [44,55] dataset consists of 499 projects and the input features considered are presented in Table 5. This value at the output layer is the total effort required to build the software.…”

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

“…In addition to the method of clustering, the different nature of the data needs to be further homogenized. This will be achieved by using the method of fuzzification [ 48 , 49 ], which involves mapping all three inputs. E, PEMi, and KLOC, into real values from the interval [0,1].…”

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

“…2. All input values are transformed according to the following formula: The function µD(X):R → [0, 1], translates the real values of input signals into coded values from the interval [0, 1], in the following way (Chhabra & Singh, 2020;Kataev et al, 2020): µD(X i ) = (X i − X min )/(X max − X min ), where D is the set of data on which the experiment is performed, X i is the input value, X min is the smallest input value, and X max the greatest input value on the observed dataset.…”