A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2016

A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity

Stamatia Bibi
  • Fonction : Auteur
  • PersonId : 989134
Apostolos Ampatzoglou
  • Fonction : Auteur
  • PersonId : 989135
Ioannis Stamelos
  • Fonction : Auteur
  • PersonId : 989136

Résumé

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.
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Dates et versions

hal-01369049 , version 1 (20-09-2016)

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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⟩
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