Neural Network Models for Agile Software Effort Estimation based on Story Points

ADITI, PANDA and SANTANU, KUMAR RATH and SHASHANK, MOULI SATAPATHY (2015) Neural Network Models for Agile Software Effort Estimation based on Story Points. In: International Conference on Advances in Computing, Control and Networking - ACCN 2015, 21 - 22 February 2015, Hotel Lebua at State Tower.

[img] Text
20150321_115000.pdf - Published Version

Download (720kB)
Official URL:


Agile software development is now accepted as a superior alternative to conventional methods of software development, because of its inherent benefits like iterative development, rapid delivery and reduced risk. Hence, the industry must be able to efficiently estimate the effort necessary to develop projects using agile methodology. For this, different techniques like expert opinion, analogy, disaggregation etc. are adopted by researchers and practitioners. But no proper mathematical model exists for this. The existing techniques are ad-hoc and are thus prone to be incorrect. One popular approach of calculating effort of agile projects mathematically is the Story Point Approach (SPA). In this study, an effort has been made to improve the prediction accuracy of estimation done using SPA. For doing this, different types of neural networks (General Regression Neural Network (GRNN), Group Method of Data Handling (GMDH) Polynomial Neural Network and Cascade- Correlation Neural Network) are used. Finally, performance of models generated using these neural networks are compared and analyzed.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Agile Software Development, General Regression Neural Network, GMDH Polynomial Neural Network, Cascade Correlation Neural Network, Software Effort Estimation, Story Point Approach
Depositing User: Mr. John Steve
Date Deposited: 09 May 2019 11:06
Last Modified: 09 May 2019 11:06

Actions (login required)

View Item View Item