@inbook {Bibi2016,
	title = {A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity},
	booktitle = {Open Source Systems: Integrating Communities: 12th IFIP WG 2.13 International Conference, OSS 2016, Gothenburg, Sweden, May 30 - June 2, 2016, Proceedings},
	year = {2016},
	pages = {32{\textendash}44},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	chapter = {A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity},
	address = {Cham},
	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.
},
	isbn = {978-3-319-39225-7},
	doi = {10.1007/978-3-319-39225-7_3},
	url = {http://dx.doi.org/10.1007/978-3-319-39225-7_3},
	author = {Bibi, Stamatia and Apostolos Ampatzoglou and Ioannis Stamelos},
	editor = {Kevin Crowston and Hammouda, Imed and Lundell, Bj{\"o}rn and Gregorio Robles and Gamalielsson, Jonas and Juho Lindman}
}
@conference {1214,
	title = {A Preliminary Analysis of Publicly Available FLOSS Measurements: Towards Discovering Maintainability Trends},
	booktitle = {2nd Workshop on Public Data about Software Development (WoPDaSD 2007)},
	year = {2007},
	note = {used SourceKibitzer data downloaded from FLOSSmole},
	month = {2007},
	abstract = {The spread of free/libre/open source software (FLOSS) and the openness of its development model offer researchers a valuable source of information regarding software data. The creation of large portals, which host a vast amount of FLOSS projects make it easy to create large datasets with valuable information regarding the FLOSS development process. In addition initiatives such as FLOSSMole provide researchers with a single point and continuing access to those data. Up to now the majority of datasets from FLOSSMole offered data regarding the development process and not the code itself. From February 2007 FLOSSMole offers data donated 
by SourceKibitzer, which contain source code metrics for FLOSS projects written in Java. In this paper we provide 
a premilinary analysis on those data using machine learning techniques, such as classification rules and decision trees. Using the first available data from February 2007, we tried to build rules that can be used in order to estimate the future values of metrics offered for March. Here we present some preliminary results that are encouraging and deserve to be further analyzed in future releases of SourceKibitzer datasets. },
	keywords = {decision tree, flossmole, java, machine learning, metrics, sourcekibitzer},
	attachments = {https://flosshub.org/sites/flosshub.org/files/Samolades2007.pdf},
	author = {Samoladas, Ioannis and Bibi, Stamatia and Ioannis Stamelos and Sowe, Sulayman K. and Deligiannis, Ignatios}
}
