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Effort Estimation in Agile Software Development using Evolutionary CostSensitive Deep Belief Network

In today’s scenario, frequent requirement changes in software development are a notable issue in the software field. Because of the frequent changes, fulfilling the user’s requirement is very difficult. As a solution to such issues, Agile Software Development (ASD) has efficiently replaced the traditional methods of software development in industries. Due to various aspects of ASD, it is extremely hard to follow, maintain and estimate the general item. Hence, in order to tackle the Effort Estimation Problem (EEP) in ASD, various types of EEP have been identified in existing methods. The Evolutionary Cost-Sensitive Deep Belief Network (ECS-DBN) model implemented in this paper for effort prediction in any agile technique. The ECS-DBN method has no impact on agility because it uses simple and small inputs. The proposed method used in planning stage of software development to support the project managers in further development of agile software. The project managers characterize the structure of the ECS-DBN, while the parameter estimation consequently gained from a dataset. This paper used different statistics like accuracy, prediction at m level to evaluate the accuracy of the model. The ECS-DBN method achieved nearly 99% accuracy compared to the existing methods.