@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}
}
@conference {940,
	title = {Using Latent Dirichlet Allocation for automatic categorization of software},
	booktitle = {2009 6th IEEE International Working Conference on Mining Software Repositories (MSR)2009 6th IEEE International Working Conference on Mining Software Repositories},
	year = {2009},
	pages = {163 - 166},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, BC, Canada},
	abstract = {In this paper, we propose a technique called LACT for automatically categorizing software systems in open-source repositories. LACT is based on latent Dirichlet Allocation, an information retrieval method which is used to index and analyze source code documents as mixtures of probabilistic topics. For an initial evaluation, we performed two studies. In the first study, LACT was compared against an existing tool, MUDABlue, for classifying 41 software systems written in C into problem domain categories. The results indicate that LACT can automatically produce meaningful category names and yield classification results comparable to MUDABlue. In the second study, we applied LACT to 43 software systems written in different programming languages such as C/C++, Java, C$\#$, PHP, and Perl. The results indicate that LACT can be used effectively for the automatic categorization of software systems regardless of the underlying programming language or paradigm. Moreover, both studies indicate that LACT can identify several new categories that are based on libraries, architectures, or programming languages, which is a promising improvement as compared to manual categorization and existing techniques.},
	keywords = {categorization, category mining, lact, mudablue, multiple languages, repository},
	isbn = {978-1-4244-3493-0},
	doi = {10.1109/MSR.2009.5069496},
	attachments = {https://flosshub.org/sites/flosshub.org/files/163MSR2009_TianPos.pdf},
	author = {Tian, Kai and Revelle, Meghan and Poshyvanyk, Denys}
}
