How Do Oss Projects Change in Number and Size? A Large-Scale Analysis to Test a Model of Project Growth

Schweitzer, Frank; Nanumyan, Vahan; Tessone, Claudio J.; Xia, Xi

doi:10.1142/s0219525915500083

Cited by 9 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This confirms previous results on human activity in these and other systems (see e.g. [26,32,34] Conversely, conditional size distributions at fixed number of contributors show a striking regularity. Rescaling size with an appropriate power of n exposes a universal curve common to all marginal distributions (separately for the two model systems, Fig.…”

Section: Effort and Number Of Contributors Are Widely Distributedsupporting

confidence: 91%

“…2), where N 1 is the number of one-man projects. Such a wide distribution has been already noted, and may reflect preferential-attachment dynamics [32], or the variable intrinsic appeal of projects [33]. A relevant additional observation regards the distribution of contributor activity, estimated by the total number of edits per contributor: it has a large-activity tail that follows a power law P (A) ∼ A −(α+1) (Fig.…”

Section: Effort and Number Of Contributors Are Widely Distributedmentioning

confidence: 64%

See 1 more Smart Citation

Law of corresponding states for open collaborations

Bassetti

Lagomarsino

2016

Phys. Rev. E

View full text Add to dashboard Cite

We study the relation between number of contributors and product size in Wikipedia and GitHub. In contrast to traditional production, this is strongly probabilistic, but is characterized by two quantitative nonlinear laws: a power-law bound to product size for increasing number of contributors, and the universal collapse of rescaled distributions. A variant of the random-energy model shows that both laws are due to the heterogeneity of contributors, and displays an intriguing finite-size scaling property with no equivalent in standard systems. The analysis uncovers the right intensive densities, enabling the comparison of projects with different numbers of contributors on equal grounds. We use this property to expose the detrimental effects of conflicting interactions in Wikipedia.

show abstract

Section: Effort and Number Of Contributors Are Widely Distributedsupporting

confidence: 91%

Section: Effort and Number Of Contributors Are Widely Distributedmentioning

confidence: 64%

Law of corresponding states for open collaborations

Bassetti

Lagomarsino

2016

Phys. Rev. E

View full text Add to dashboard Cite

show abstract

“…Empirically, software networks keep growing in response to changing conditions and new requirements, in line with the empirical studies on a large number of real software systems [48,49]. Consequently, new functional modules are continually added into software systems, and these elements are much more than those that are removed.…”

Section: The Mechanism Of Software Evolutionmentioning

confidence: 74%

Multi-Level Formation of Complex Software Systems

Hao

Chen

2016

Entropy

View full text Add to dashboard Cite

We present a multi-level formation model for complex software systems. The previous works extract the software systems to software networks for further studies, but usually investigate the software networks at the class level. In contrast to these works, our treatment of software systems as multi-level networks is more realistic. In particular, the software networks are organized by three levels of granularity, which represents the modularity and hierarchy in the formation process of real-world software systems. More importantly, simulations based on this model have generated more realistic structural properties of software networks, such as power-law, clustering and modularization. On the basis of this model, how the structure of software systems effects software design principles is then explored, and it could be helpful for understanding software evolution and software engineering practices.

show abstract

“…a projection of commits where two developers are linked if they committed to the same Open Source project. Schweitzer et al (2014) provided a related study, analysing ten years of data from the Open Source project hosting platform SourceForge. These works have typically constructed undirected co-authorship networks based on joint contributions to files, modules, or projects. Such coarse-grained definitions of coauthorship networks introduce a potential issue: They do not distinguish between (i) links between developers that are due to independent contributions to the same artefact, and (ii) links that are due to commit sequences where one developer builds upon and/or redacts the particular lines of source code previously authored by another developer.…”

Section: Constructing Social Network From Software Repositoriesmentioning

confidence: 99%

Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net

2021

Self Cite

View full text Add to dashboard Cite

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Many works in this area studied networks of co-authorship of software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, we introduce , a scalable software that facilitates the extraction of fine-grained co-editing networks in large repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. We apply our tool in two case studies using repositories of multiple Open Source as well as a proprietary software project. Specifically, we use data on more than 1.2 million commits and more than 25,000 developers to test a hypothesis on the relation between developer productivity and co-editing patterns in software teams. We argue that opens up an important new source of high-resolution data on human collaboration patterns that can be used to advance theory in empirical software engineering, computational social science, and organisational studies.

show abstract

How Do Oss Projects Change in Number and Size? A Large-Scale Analysis to Test a Model of Project Growth

Cited by 9 publications

References 15 publications

Law of corresponding states for open collaborations

Law of corresponding states for open collaborations

Multi-Level Formation of Complex Software Systems

Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net

Contact Info

Product

Resources

About