Empirical assessment of machine learning models for agile software development effort estimation using story points

2017 ◽  
Vol 13 (2-3) ◽  
pp. 191-200 ◽  
Author(s):  
Shashank Mouli Satapathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Software Development ◽  
Agile Software Development ◽  
Development Effort ◽  
Effort Estimation ◽  
Learning Models ◽  
Software Development Effort ◽  
Agile Software ◽  
Machine Learning Models ◽  
Software Development Effort Estimation

Software Effort Estimation

2011 ◽  
Vol 7 (3) ◽  
pp. 41-53 ◽  
Author(s):  
Jeremiah D. Deng ◽  
Martin Purvis ◽  
Maryam Purvis
Keyword(s):  
Machine Learning ◽  
Software Development ◽  
Domain Knowledge ◽  
Modeling Processes ◽  
Machine Learning Algorithms ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often cannot achieve satisfactory results. This paper presents an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the necessity and effectiveness of the proposed approach.

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