Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution

Abstract

In the process of software development it is of high interest for a project manager to gain insights about the ongoing process and possible development trends at several points in time. Substantial factors influencing this process are, e.g., the constellation of the development team, the growth and complexity of the system, and the error-proneness of software entities. For this purpose we build an agent based simulation tool which predicts the future of a project under given circumstances, stored in parameters, which control the simulation process. We estimate these parameters with the help of software mining. Our work exposed the need for a more fine-grained model for the developer behavior. Due to this we create a learning model, which helps us to understand the contribution behavior of developers and, thereby, to determine simulation parameters close to reality. In this paper we present our agent based simulation model for software evolution and describe how methods from statistical learning and data mining serves us to estimate suitable simulation parameters.
Document Type: 
Articles in Conference Proceedings
Booktitle: 
Proceedings of the 37th International Conference on Software Engineering - Volume 2
Series: 
ICSE '15
Address: 
Piscataway, NJ, USA
Publisher: 
IEEE Press
Volume: 
2
Pages: 
863--866
Year: 
2015
URL: 
http://dl.acm.org/citation.cfm?id=2819009.2819200

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