Applying a Feedforward Neural Network for Predicting Software Development Effort of Short-Scale Projects

2010 ◽  
Author(s):  
Ivica Kalichanin-Balich ◽  
Cuauhtemoc Lopez-Martin
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Feedforward Neural Network ◽  
Development Effort ◽  
Short Scale ◽  
Software Development Effort

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.

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.

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