Comparing some neural network models for software development effort prediction

2011 ◽  
Author(s):  
Mrinal Kanti Ghose ◽  
Roheet Bhatnagar ◽  
Vandana Bhattacharjee
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Network Models ◽  
Development Effort ◽  
Neural Network Models ◽  
Software Development Effort ◽  
Effort Prediction

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.

APPLYING EXPERT JUDGMENT TO IMPROVE AN INDIVIDUAL'S ABILITY TO PREDICT SOFTWARE DEVELOPMENT EFFORT

2012 ◽  
Vol 22 (04) ◽  
pp. 467-483 ◽  
Author(s):  
CUAUHTÉMOC LÓPEZ-MARTÍN ◽  
ALAIN ABRAN
Keyword(s):  
Software Development ◽  
Software Process ◽  
Development Effort ◽  
Academic Environment ◽  
Academic Institutions ◽  
Graduate Courses ◽  
Software Projects ◽  
Personal Software Process ◽  
Software Development Effort ◽  
Effort Prediction

Expert-based effort prediction in software projects can be taught, beginning with the practices learned in an academic environment in courses designed to encourage them. However, the length of such courses is a major concern for both industry and academia. Industry has to work without its employees while they are taking such a course, and academic institutions find it hard to fit the course into an already tight schedule. In this research, the set of Personal Software Process (PSP) practices is reordered and the practices are distributed among fewer assignments, in an attempt to address these concerns. This study involved 148 practitioners taking graduate courses who developed 1,036 software course assignments. The hypothesis on which it is based is the following: When the activities in the original PSP set are reordered into fewer assignments, the result is expert-based effort prediction that is statistically significantly better.

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