A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits

Fry, Tanner; Dey, Tapajit; Karnauch, Andrey; Mockus, Audris

doi:10.1145/3379597.3387500

Cited by 30 publications

(21 citation statements)

References 16 publications

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“…Dealing with duplicate or aliased entities from disparate sources of data has been discussed in literature [7,9,33]. As the final step of database preparation, LAGOON ingest batches are merged under a fusion process that de-duplicates entities across different batches by creating a single FusedEntity to represent them.…”

Section: Overview Of Lagoonmentioning

confidence: 99%

LAGOON: An Analysis Tool for Open Source Communities

Dey¹,

Woods²

2022

Preprint

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This paper presents LAGOON -an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities. The platform currently utilizes spatiotemporal graphs to store and investigate the artifacts produced by these communities, and help analysts identify bad actors who might compromise an OSS project's security. LAGOON provides ingest of artifacts from several common sources, including source code repositories, issue trackers, mailing lists and scraping content from project websites. Ingestion utilizes a modular architecture, which supports incremental updates from data sources and provides a generic identity fusion process that can recognize the same community members across disparate accounts. A user interface is provided for visualization and exploration of an OSS project's complete sociotechnical graph. Scripts are provided for applying machine learning to identify patterns within the data. While current focus is on the identification of bad actors in the Python community, the platform's reusability makes it easily extensible with new data and analyses, paving the way for LAGOON to become a comprehensive means of assessing various OSS-based projects and their communities.

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Section: Overview Of Lagoonmentioning

confidence: 99%

LAGOON: An Analysis Tool for Open Source Communities

Dey¹,

Woods²

2022

Preprint

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“…The disambiguation approach used in this analysis is based on simple heuristics and is therefore able to scale well to the large data sets used in our study. However, since conducting our study, new advanced disambiguation algorithms and data sets have been made available which should be considered for future studies (Amreen et al 2020;Fry et al 2020).…”

Section: Construct Validitymentioning

confidence: 99%

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

2021

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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.

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“…One paper explicitly mentioned that the data was used with consent of the organisation behind it (Gonzalez-Barahona et al 2015). One paper did discuss ethics issues and described how the data was pseudonymised (Fry et al 2020). Another paper had a detailed discussion on privacy, and legal and ethics issues (Robles et al 2014).…”

Section: Data Showcasementioning

confidence: 99%

Ethics in the mining of software repositories

Gold

Krinke

2021

Empir Software Eng

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Research in Mining Software Repositories (MSR) is research involving human subjects, as the repositories usually contain data about developers’ and users’ interactions with the repositories and with each other. The ethics issues raised by such research therefore need to be considered before beginning. This paper presents a discussion of ethics issues that can arise in MSR research, using the mining challenges from the years 2006 to 2021 as a case study to identify the kinds of data used. On the basis of contemporary research ethics frameworks we discuss ethics challenges that may be encountered in creating and using repositories and associated datasets. We also report some results from a small community survey of approaches to ethics in MSR research. In addition, we present four case studies illustrating typical ethics issues one encounters in projects and how ethics considerations can shape projects before they commence. Based on our experience, we present some guidelines and practices that can help in considering potential ethics issues and reducing risks.

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A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits

Cited by 30 publications

References 16 publications

LAGOON: An Analysis Tool for Open Source Communities

LAGOON: An Analysis Tool for Open Source Communities

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

Ethics in the mining of software repositories

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