Paper Info
Title
A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity.
Abstract
Maintenance is one of the most effort consuming activities in the software development lifecycle. Efficient maintenance within short release cycles depends highly on the underlying source code structure, in the sense that complex modules are more difficult to maintain. In this paper we attempt to unveil and discuss relationships between maintenance productivity, the structural quality of the source code and process metrics like the type of a release and the number of downloads. To achieve this goal, we developed a Bayesian Belief Network (BBN) involving several maintainability predictors and three managerial indices for maintenance (i.e., duration, production, and productivity) on 20 open source software projects. The results suggest that maintenance duration depends on inheritance, coupling, and process metrics. On the other hand maintenance production and productivity depend mostly on code quality metrics.
Year
DOI
Venue
2016
10.1007/978-3-319-39225-7_3
IFIP Advances in Information and Communication Technology
Keywords
Field
DocType
Maintenance,Productivity,Software metrics,Bayesian networks
Systems engineering,Computer science,Source code,Bayesian network,Software development process,Software metric,Software quality,Open source software,Reliability engineering,Maintainability
Conference
Volume
ISSN
Citations 
472
1868-4238
2
PageRank 
References 
Authors
0.37
15
3
Name
Order
Citations
PageRank
Stamatia Bibi15310.10
Apostolos Ampatzoglou233441.24
Ioannis Stamelos3124391.67