Analysis and Comparison of Neural Network Models for Software Development Effort Estimation

2022 ◽  
pp. 165-193
Author(s):  
Kamlesh Dutta ◽  
Varun Gupta ◽  
Vachik S. Dave
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Network Models ◽  
Training Data ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Neural Network Models ◽  
Software Development Effort ◽  
Artificial Neural Network Ann

Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.

Analysis and Comparison of Neural Network Models for Software Development Effort Estimation

2019 ◽  
Vol 21 (2) ◽  
pp. 88-112
Author(s):  
Kamlesh Dutta ◽  
Varun Gupta ◽  
Vachik S. Dave
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Network Models ◽  
Training Data ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Neural Network Models ◽  
Data Set ◽  
Software Development Effort

Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.

K-Means Clustering Algorithms to Compute Software Effort Estimation

2016 ◽  
Vol 13 (10) ◽  
pp. 7093-7098 ◽  
Author(s):  
Shivakumar Nagarajan ◽  
Balaji Narayanan
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Clustering Algorithms ◽  
Uncertain Data ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Development Effort

Software development effort estimation is the way of predicting the effort to improve software economics. Accurate estimation of effort is the most tedious tasks in software projects. However, several methods are used to estimate the software development effort accurately. Imprecise estimation can leads to project failure due to uncertain data. In this paper, a hybrid model based on combination of Particle Swarm Optimization (PSO), K-means clustering algorithms, neural network and ABE method is proposed. The proposed method can be useful to predict better clustering and more accurate estimation and hence, there are difficulties in clustering and outliers in the software projects. The obtained results showed the better clustering result which provides the estimation result accurately. Then, neural network and Analogy methods are used which enhance the accuracy significantly.

scholarly journals Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Mahmoud O. Elish ◽  
Tarek Helmy ◽  
Muhammad Imtiaz Hussain
Keyword(s):  
Empirical Study ◽  
Software Development ◽  
Computational Intelligence ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Ensemble Models ◽  
Intelligence Models ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Accurate estimation of software development effort is essential for effective management and control of software development projects. Many software effort estimation methods have been proposed in the literature including computational intelligence models. However, none of the existing models proved to be suitable under all circumstances; that is, their performance varies from one dataset to another. The goal of an ensemble model is to manage each of its individual models’ strengths and weaknesses automatically, leading to the best possible decision being taken overall. In this paper, we have developed different homogeneous and heterogeneous ensembles of optimized hybrid computational intelligence models for software development effort estimation. Different linear and nonlinear combiners have been used to combine the base hybrid learners. We have conducted an empirical study to evaluate and compare the performance of these ensembles using five popular datasets. The results confirm that individual models are not reliable as their performance is inconsistent and unstable across different datasets. Although none of the ensemble models was consistently the best, many of them were frequently among the best models for each dataset. The homogeneous ensemble of support vector regression (SVR), with the nonlinear combiner adaptive neurofuzzy inference systems-subtractive clustering (ANFIS-SC), was the best model when considering the average rank of each model across the five datasets.

scholarly journals Models for Predicting Development Effort of Small-Scale Visualization Projects

2018 ◽  
Vol 27 (3) ◽  
pp. 413-431
Author(s):  
M.A. Jayaram ◽  
T.M. Kiran Kumar ◽  
H.V. Raghavendra
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Models ◽  
Small Scale ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Neural Network Models ◽  
Grade Point ◽  
Postgraduate Students

Abstract Software project effort estimation is one of the important aspects of software engineering. Researchers in this area are still striving hard to come out with the best predictive model that has befallen as a greatest challenge. In this work, the effort estimation for small-scale visualization projects all rendered on engineering, general science, and other allied areas developed by 60 postgraduate students in a supervised academic setting is modeled by three approaches, namely, linear regression, quadratic regression, and neural network. Seven unique parameters, namely, number of lines of code (LOC), new and change code (N&C), reuse code (R), cumulative grade point average (CGPA), cyclomatic complexity (CC), algorithmic complexity (AC), and function points (FP), which are considered to be influential in software development effort, are elicited along with actual effort. The three models are compared with respect to their prediction accuracy via the magnitude of error relative to the estimate (MER) for each project and also its mean MER (MMER) in all the projects in both the verification and validation phases. Evaluations of the models have shown MMER of 0.002, 0.006, and 0.009 during verification and 0.006, 0.002, and 0.002 during validation for the multiple linear regression, nonlinear regression, and neural network models, respectively. Thus, the marginal differences in the error estimates have indicated that the three models can be alternatively used for effort computation specific to visualization projects. Results have also suggested that parameters such as LOC, N&C, R, CC, and AC have a direct influence on effort prediction, whereas CGPA has an inverse relationship. FP seems to be neutral as far as visualization projects are concerned.

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