@conference {1216, title = {Experiences Mining Open Source Release Histories}, booktitle = {International Conference on Software and Systems Process (ICSSP 2011) }, year = {2011}, note = {"First, we selected the projects to initially target, using several criteria to get a broad picture of the open source landscape. Second, we collected the actual data, using a framework of parsers and some manual inspection. Third, we standardized and inserted the data into a database for later use." "but we plan to eventually cross reference our list of projects with existing open source project information (such as FLOSSmole) to take advantage of the work already done by other researchers." "For each release, we collected the following data: the project it belonged to, the date the release was published, the type of release, the release label (version number) and the source of the data" discussion of their difficulties "We conclude that programmatically creating a release history database from existing open source data is not trivial," "We have currently collected 1579 distinct releases from 22 different open source projects"}, month = {05/2011}, abstract = {Software releases form a critical part of the life cycle of a software project. Typically, each project produces releases in its own way, using various methods of versioning, archiving, announcing and publishing the release. Understanding the release history of a software project can shed light on the project history, as well as the release process used by that project, and how those processes change. However, many factors make automating the retrieval of release history information difficult, such as the many sources of data, a lack of relevant standards and a disparity of tools used to create releases. In spite of the large amount of raw data available, no attempt has been made to create a release history database of a large number of projects in the open source ecosystem. This paper presents our experiences, including the tools, techniques and pitfalls, in our early work to create a software release history database which will be of use to future researchers who want to study and model the release engineering process in greater depth.}, keywords = {doap, flossmole cited, life cycle, release engineering, release history, release management, releases}, attachments = {https://flosshub.org/sites/flosshub.org/files/icssp11short-p034-tsay.pdf}, author = {Jason Tsay and Wright, Hyrum and Perry, Dewayne} } @conference {1258, title = {Exploring Complexity in Open Source Software: Evolutionary Patterns, Antecedents, and Outcomes}, booktitle = {2010 43rd Hawaii International Conference on System Sciences (HICSS 2010)}, year = {2010}, note = {"The sample of projects was drawn from SourceForge" "projects were selected that were built with C++." "Applying the selection criteria generated a total of 108 projects for analysis" "Scientific Toolwork{\textquoteright}s Understand (version 1.4)"}, pages = {1 - 11}, publisher = {IEEE}, organization = {IEEE}, address = {Honolulu, Hawaii, USA}, abstract = {Software complexity is important to researchers and managers, yet much is unknown about how complexity evolves over the life of a software application and whether different dimensions of software complexity may exhibit similar or different evolutionary patterns. Using cross-sectional and longitudinal data on a sample of 108 open source projects, this research investigated how the complexity of open source project releases varied throughout the life of the project. Functional data analysis was applied to the release histories of the projects and recurring evolutionary patterns were derived. There were projects that saw little evolution, according to their measures of size and structural complexity. However, projects that displayed some evolution often differed on the pattern of evolution depending on whether size or structural complexity was examined. Factors that contribute to and result from the patterns of complexity were evaluated, and implications for research and practice are presented.}, keywords = {complexity, evolution, fda, life cycle, sourceforge, srda}, isbn = {978-1-4244-5509-6}, doi = {10.1109/HICSS.2010.198}, attachments = {https://flosshub.org/sites/flosshub.org/files/10-07-02.pdf}, author = {Darcy, David P. and Daniel, Sherae L. and Stewart, Katherine J.} } @conference {1206, title = {Collecting data from distributed FOSS projects}, booktitle = {3rd Workshop on Public Data about Software Development (WoPDaSD 2008)}, year = {2008}, note = {"We selected three projects from the initial set of projects: Linux 2.6, an operating system kernel, gimp, a graphics program, and Blender, a 3d content creation suite." "To acquire data from each data source, we wrote special programs based on the earlier prototypes....The first program extracts information from mailing list archives....The second program obtains bug reports from bug tracking systems....The third program obtains source code from network-accessible version control systems and runs metric calculations on it."}, month = {2009}, pages = {8-13}, abstract = {A key trait of Free and Open Source Software (foss) development is its distributed nature. Nevertheless, two project-level operations, the fork and the merge of program code, are among the least well understood events in the lifespan of a foss project. Some projects have explicitly adopted these operations as the primary means of concurrent development. In this study, we examine the effect of highly distributed software development, as found in the Linux kernel project, on collection and modelling of software development data. We find that distributed development calls for sophisticated temporal modelling techniques where several versions of the source code tree can exist at once. Attention must be turned towards the methods of quality assurance and peer review that projects employ to manage these parallel source trees. Our analysis indicates that two new metrics, fork rate and merge rate, could be useful for determining the role of distributed version control systems in foss projects. The study presents a preliminary data set consisting of version control and mailing list data. }, keywords = {bitkeeper, bug tracking system, cvs, distributed, email archive, fork rate, git, life cycle, linux, linux kernel, mailing list, merge rate, subversion, svn, version control}, attachments = {https://flosshub.org/sites/flosshub.org/files/fagerholm.pdf}, author = {Fagerholm, Fabian and Taina, Juha} } @proceedings {1197, title = {Open Source and Closed Source Software Development Methodologies}, year = {2004}, pages = {105-109}, abstract = {Open source software development represents a fundamentally new concept in the field of software engineering. Open source development and delivery occurs over the Internet. Developers are not confined to a geographic area. They work voluntarily on a project of their choice. As new requirements emerge, the software is enhanced by the user/developers. In this paper we show a comparative study of open source and closed source software development approaches and present a software life cycle model for open source software development.}, keywords = {life cycle, lifecycle}, attachments = {https://flosshub.org/sites/flosshub.org/files/potdar106-110.pdf}, author = {Potdar, V. and Chang, E.} } @conference {Wynn03organizationalstructure, title = {Organizational Structure of Open Source Projects: A Life Cycle Approach}, booktitle = {Proceedings of 7th Annual Conference of the Southern Association for Information Systems}, year = {2003}, note = {"The three graphs in Figure 2 below were taken from smoothed download counts for existing open source projects on Sourceforge.net" "A random sample of 150 open source projects will be taken from data provided by Sourceforge.net. Each project will be evaluated to determine their current life cycle stage (where possible) using download counts. Next, the project admins, developers, and several identifiable users for each evaluated project will be contacted via email to request completing a brief questionnaire to measure the current focus of the project, formal structure, division of labor, leader role, coordination, level of commitment, user success, and developer success. "}, abstract = {The structure of open source project communities is discussed in relation to the organizational life cycle. In lieu of sales figures, the download counts for each project are used to identify the life cycle stage of a random sample of open source projects. A research model is proposed that attempts to measure the fit between the life cycle stage and the specific organizational characteristics of these projects (focus, division of labor, role of the leader, level of commitment, and coordination/control) as an indicator of the success of a project as measured by the satisfaction and involvement of both developers and users.}, keywords = {division of labor, downloads, growth, interview, leadership, life cycle, lifecycle, project success, roles, sourceforge, Survey}, attachments = {https://flosshub.org/sites/flosshub.org/files/wynn2004.pdf}, author = {Donald E. Wynn} }