Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [27 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Tuesday, September 20, 2016 - 2:14:05 PM
Last modification on : Tuesday, January 5, 2021 - 5:10:03 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Stamatia Bibi, Apostolos Ampatzoglou, Ioannis Stamelos. A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity. 12th IFIP International Conference on Open Source Systems (OSS), May 2016, Gothenburg, Sweden. pp.32-44, ⟨10.1007/978-3-319-39225-7_3⟩. ⟨hal-01369049⟩



Record views


Files downloads