@proceedings {1911,
	title = {Machine Learning-Based Detection of Open Source License Exceptions},
	year = {2017},
	note = {"We address these questions by first performing a large scale mining-based study... [W]e analyzed the  source code of 51,754 projects written in six different programming languages (Ruby, Javascript, Python, C, C++, and C$\#$) hosted on GitHub. },
	month = {05/2017},
	pages = {118-129},
	abstract = {From a legal perspective, software licenses govern the redistribution, reuse, and modification of software as both source and binary code. Free and Open Source Software (FOSS) licenses vary in the degree to which they are permissive or restrictive in allowing redistribution or modification under licenses different from the original one(s). In certain cases developers may modify the license by appending to it an exception to specifically allow reuse or modification under a particular condition. These exceptions are an important factor to consider for license compliance analysis since they modify the standard (and widely understood_ terms of the original license. In this work, we first perform a large-scale empirical study on the change history of over 51k FOSS systems aimed at quantitatively investigating the prevalence of known license exceptions and identifying new ones. Subsequently, we performed a study on the detection of license exceptions by relying on machine learning. We evaluated the license exception classification with four different supervised learners and sensitivity analysis. Finally we present a categorization of license exceptions and explain their implications.},
	keywords = {classifier, empirical studies, license, machine learning},
	doi = {10.1109/ICSE.2017.19},
	author = {Vendome, Christopher and Mario Linares-Vasquez and Bavota, Gabriele and Di Penta, Massimiliano and Daniel M. German and Poshyvanyk, Denys}
}
@article {1847,
	title = {License usage and changes: a large-scale study on gitHub},
	journal = {Empirical Software Engineering},
	year = {2016},
	abstract = {Open source software licenses determine, from a legal point of view, under which conditions software can be integrated and redistributed. The reason why developers of a project adopt (or change) a license may depend on various factors, e.g., the need for ensuring compatibility with certain third-party components, the perspective towards redistribution or commercialization of the software, or the need for protecting against somebody else{\textquoteright}s commercial usage of the software. This paper reports a large empirical study aimed at quantitatively and qualitatively investigating when and why developers adopt or change software licenses. Specifically, we first identify license changes in 1,731,828 commits, representing the entire history of 16,221 Java projects hosted on GitHub. Then, to understand the rationale of license changes, we perform a qualitative analysis on 1,160 projects written in seven different programming languages, namely C, C++, C$\#$, Java, Javascript, Python, and Ruby{\textemdash}following an open coding approach inspired by grounded theory{\textemdash}on commit messages and issue tracker discussions concerning licensing topics, and whenever possible, try to build traceability links between discussions and changes. On one hand, our results highlight how, in different contexts, license adoption or changes can be triggered by various reasons. On the other hand, the results also highlight a lack of traceability of when and why licensing changes are made. This can be a major concern, because a change in the license of a system can negatively impact those that reuse it. In conclusion, results of the study trigger the need for better tool support in guiding developers in choosing/changing licenses and in keeping track of the rationale of license changes.},
	issn = {1573-7616},
	doi = {10.1007/s10664-016-9438-4},
	author = {Vendome, Christopher and Bavota, Gabriele and Di Penta, Massimiliano and Linares-V{\'a}squez, Mario and German, Daniel and Poshyvanyk, Denys}
}
@proceedings {1729,
	title = {A Large Scale Study of License Usage on GitHub},
	volume = {2},
	year = {2015},
	note = {14.9\% of projects have a license file.},
	month = {05/2015},
	pages = {772-774},
	publisher = {ACM/IEEE},
	abstract = {The open source community relies upon licensing
in order to govern the distribution, modification, and reuse of
existing code. These licenses evolve to better suit the requirements
of the development communities and to cope with unaddressed
or new legal issues. In this paper, we report the results of a large
empirical study conducted over the change history of 16,221 open
source Java projects mined from GitHub. Our study investigates
how licensing usage and adoption changes over a period of ten
years. We consider both the distribution of license usage within
projects of a rapidly growing forge and the extent that new
versions of licenses are introduced in these projects.},
	keywords = {github, license},
	author = {Vendome, Christopher}
}
