@conference {Izquierdo-Cortazar:2017:UMT:3084226.3084247,
	title = {Using Metrics to Track Code Review Performance},
	booktitle = {Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering},
	series = {EASE{\textquoteright}17},
	year = {2017},
	pages = {214{\textendash}223},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {During 2015, some members of the Xen Project Advisory Board became worried about the performance of their code review process. The Xen Project is a free, open source software project developing one of the most popular virtualization platforms in the industry. They use a pre-commit peer review process similar to that in the Linux kernel, based on email messages. They had observed a large increase over time in the number of messages related to code review, and were worried about how this could be a signal of problems with their code review process.

To address these concerns, we designed and conducted, with their continuous feedback, a detailed analysis focused on finding these problems, if any. During the study, we dealt with the methodological problems of Linux-like code review, and with the deeper issue of finding metrics that could uncover the problems they were worried about. For having a benchmark, we run the same analysis on a similar project, which uses very similar code review practices: the Linux Netdev (Netdev) project. As a result, we learned how in fact the Xen Project had some problems, but at the moment of the analysis those were already under control. We found as well how different the Xen and Netdev projects were behaving with respect to code review performance, despite being so similar from many points of view.

In this paper we show the results of both analyses, and propose a comprehensive methodology, fully automated, to study Linux-style code review. We discuss also the problems of getting significant metrics to track improvements or detect problems in this kind of code review.},
	keywords = {code review, data mining, Software development analytics},
	isbn = {978-1-4503-4804-1},
	doi = {10.1145/3084226.3084247},
	url = {http://doi.acm.org/10.1145/3084226.3084247},
	author = {Izquierdo-Cortazar, Daniel and Sekitoleko, Nelson and Jesus M. Gonzalez-Barahona and Kurth, Lars}
}
@conference {Matragkas:2014:ABO:2597073.2597119,
	title = {Analysing the {\textquoteright}Biodiversity{\textquoteright} of Open Source Ecosystems: The GitHub Case},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	pages = {356{\textendash}359},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In nature the diversity of species and genes in ecological communities affects the functioning of these communities. Biologists have found out that more diverse communities appear to be more productive than less diverse communities. Moreover such communities appear to be more stable in the face of perturbations. In this paper, we draw the analogy between ecological communities and Open Source Software (OSS) ecosystems, and we investigate the diversity and structure of OSS communities. To address this question we use the MSR 2014 challenge dataset, which includes data from the top-10 software projects for the top programming languages on GitHub. Our findings show that OSS communities on GitHub consist of 3 types of users (core developers, active users, passive users). Moreover, we show that the percentage of core developers and active users does not change as the project grows and that the majority of members of large projects are passive users.
},
	keywords = {Data and knowledge visualization, data mining, mining challenge, msr challenge},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597119},
	url = {http://doi.acm.org/10.1145/2597073.2597119},
	author = {Matragkas, Nicholas and Williams, James R. and Kolovos, Dimitris S. and Paige, Richard F.}
}
@proceedings {1573,
	title = {"A bit of code": How the Stack Overflow Community Creates Quality Postings},
	year = {2014},
	month = {01/2014},
	pages = {1425-1434},
	publisher = {IEEE Computer Society},
	abstract = {The Stack Overflow web site is an online community where programmers can ask and answer one another{\textquoteright}s questions, earning points and badges. The site offers guidance in the form of a Frequently Asked Questions (FAQ), beginning with "What kind of questions can I ask here?" The answer explains that "the best Stack Overflow questions have a bit of source code in them". This paper explores the role of source code and non-source code text on Stack Overflow in both questions and answers. The primary contribution of this paper is to provide a more detailed understanding of whether the presence of source code (and how much) actually will produce the "best" Stack Overflow questions or answers. A second contribution of this paper is to determine how the non-code portions of the text might also contribute the "best" Stack Overflow postings.  },
	keywords = {COLLABORATION, collaborative development, data mining, developer network, knowledge collaboration, open content, text mining},
	doi = {http://dx.doi.org/10.1109/HICSS.2014.185},
	attachments = {https://flosshub.org/sites/flosshub.org/files/hicssSMFinalWatermark.pdf},
	author = {Squire, Megan and Funkhouser, Christian}
}
@conference {Williams:2014:MOP:2597073.2597132,
	title = {Models of OSS Project Meta-information: A Dataset of Three Forges},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	note = {"FLOSSMole [4] is a similar initiative to OSSMETER; it
aims to collect and freely redistribute in different formats
the data of open source software. Differently from OSSMETER,
however, the FLOSSMole project does not provide the
instruments to analyse data, that are simply collected and
made publicly available."},
	pages = {408{\textendash}411},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {The process of selecting open-source software (OSS) for adoption is not straightforward as it involves exploring various sources of information to determine the quality, maturity, activity, and user support of each project. In the context of the OSSMETER project, we have developed a forge-agnostic metamodel that captures the meta-information common to all OSS projects. We specialise this metamodel for popular OSS forges in order to capture forge-specific meta-information. In this paper we present a dataset conforming to these metamodels for over 500,000 OSS projects hosted on three popular OSS forges: Eclipse, SourceForge, and GitHub. The dataset enables different kinds of automatic analysis and supports objective comparisons of cross-forge OSS alternatives with respect to a user{\textquoteright}s needs and quality requirements.
},
	keywords = {data mining, flossmole cited},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597132},
	url = {http://doi.acm.org/10.1145/2597073.2597132},
	attachments = {https://flosshub.org/sites/flosshub.org/files/Models_of_OSS_Project_Meta-Information_A_Dataset_of_Three_Forges_draft.pdf},
	author = {Williams, James R. and Di Ruscio, Davide and Matragkas, Nicholas and Di Rocco, Juri and Kolovos, Dimitris S.}
}
@article {1330,
	title = {Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems},
	journal = {International Journal of Open Source Software and Processes},
	volume = {2},
	year = {2010},
	pages = {65 - 75},
	abstract = {Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.},
	keywords = {data mining, education, student},
	issn = {1942-3934},
	doi = {10.4018/jossp.2010010105},
	author = {McGrath, Owen}
}
@conference {bird2010lee,
	title = {{Linkster: Enabling Efficient Manual Mining}},
	booktitle = {Demonstration Track, Proceedings of the 17th SIGSOFT Symposium on Foundations of Software Engineering},
	year = {2010},
	note = {"LINKSTER efficiently displays, integrates, and allows inspection and annotation of information from three main sources of data: source code repositories, developer mailing lists archives, and bug tracking databases. LINKSTER requires access to a source code repository for file content and a database which contains the raw mined repository, mailing list, and bug tracking information. All notes and annotations made by the user are also recorded in the database."},
	publisher = {ACM},
	organization = {ACM},
	abstract = {While many uses of mined software engineering data are automatic in nature, some techniques and studies either require, or can be improved, by manual methods. Unfortunately, manually inspecting, analyzing, and annotating mined data can be difficult and tedious, especially when information from multiple sources must be integrated. Oddly, while there are numerous tools and frameworks for automatically mining and analyzing data, there is a dearth of tools which facilitate manual methods. To fill this void, we have developed LINKSTER, a tool which integrates data from bug databases, source code repositories, and mailing list archives to allow manual inspection and annotation. LINKSTER has already been used successfully by an OSS project lead to obtain data for one empirical study.},
	keywords = {artifacts, bug, bug tracking, data mining, email, mailing lists, open source, source code},
	attachments = {https://flosshub.org/sites/flosshub.org/files/bird2010lee.pdf},
	author = {Christian Bird and Adrian Bachman and Rahman, Foyzur and Bernstein, Abraham}
}
@conference {Knab:2006:PDD:1137983.1138012,
	title = {Predicting defect densities in source code files with decision tree learners},
	booktitle = {Proceedings of the 2006 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}06},
	year = {2006},
	pages = {119{\textendash}125},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out to derive reasonable results the sheer amount of data and the problem context of defect prediction demand sophisticated analysis such as provided by current data mining and machine learning techniques.In this work we focus on defect density prediction and present an approach that applies a decision tree learner on evolution data extracted from the Mozilla open source web browser project. The evolution data includes different source code, modification, and defect measures computed from seven recent Mozilla releases. Among the modification measures we also take into account the change coupling, a measure for the number of change-dependencies between source files. The main reason for choosing decision tree learners, instead of for example neural nets, was the goal of finding underlying rules which can be easily interpreted by humans. To find these rules, we set up a number of experiments to test common hypotheses regarding defects in software entities. Our experiments showed, that a simple tree learner can produce good results with various sets of input data.},
	keywords = {change analysis, data mining, decision tree learner, defect density, defect prediction, mozilla, prediction, release history, scm, source code, version control},
	isbn = {1-59593-397-2},
	doi = {http://doi.acm.org/10.1145/1137983.1138012},
	url = {http://doi.acm.org/10.1145/1137983.1138012},
	attachments = {https://flosshub.org/sites/flosshub.org/files/119Predicting.pdf},
	author = {Knab, Patrick and Pinzger, Martin and Bernstein, Abraham}
}
@conference {Antoniol:2005:LPC:1083142.1083156,
	title = {Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories},
	booktitle = {Proceedings of the 2005 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}05},
	year = {2005},
	pages = {74-78},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This paper presents an approach to recover time variant information from software repositories. It is widely accepted that software evolves due to factors such as defect removal, market opportunity or adding new features. Software evolution details are stored in software repositories which often contain the changes history. On the other hand there is a lack of approaches, technologies and methods to efficiently extract and represent time dependent information. Disciplines such as signal and image processing or speech recognition adopt frequency domain representations to mitigate differences of signals evolving in time. Inspired by time-frequency duality, this paper proposes the use of Linear Predictive Coding (LPC) and Cepstrum coefficients to model time varying software artifact histories. LPC or Cepstrum allow obtaining very compact representations with linear complexity. These representations can be used to highlight components and artifacts evolved in the same way or with very similar evolution patterns. To assess the proposed approach we applied LPC and Cepstral analysis to 211 Linux kernel releases (i.e., from 1.0 to 1.3.100), to identify files with very similar size histories. The approach, the preliminary results and the lesson learned are presented in this paper.},
	keywords = {change history, data mining, evolution, files, kernel, linear predictive coding, linux, lpc, size, software evolution, source code},
	isbn = {1-59593-123-6},
	doi = {http://doi.acm.org/10.1145/1082983.1083156},
	url = {http://doi.acm.org/10.1145/1082983.1083156},
	attachments = {https://flosshub.org/sites/flosshub.org/files/74LinearPredictive.pdf},
	author = {Antoniol, Giuliano and Rollo, Vincenzo Fabio and Venturi, Gabriele}
}
