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The GitHub Development Workflow Automation Ecosystems

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arxiv 2305.04772 v2 pith:BR3E6AL3 submitted 2023-05-08 cs.SE

classification cs.SE
keywords developmentsoftwaretoolsautomationcodingecosystemsgithubactivities
verification ladder T0 review T1 audit T2 compute T3 formal

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Large-scale software development has become a highly collaborative and geographically distributed endeavour, especially in open-source software development ecosystems and their associated developer communities. It has given rise to modern development processes (e.g., pull-based development) that involve a wide range of activities such as issue and bug handling, code reviewing, coding, testing, and deployment. These often very effort-intensive activities are supported by a wide variety of tools such as version control systems, bug and issue trackers, code reviewing systems, code quality analysis tools, test automation, dependency management, and vulnerability detection tools. To reduce the complexity of the collaborative development process, many of the repetitive human activities that are part of the development workflow are being automated by CI/CD tools that help to increase the productivity and quality of software projects. Social coding platforms aim to integrate all this tooling and workflow automation in a single encompassing environment. These social coding platforms gave rise to the emergence of development bots, facilitating the integration with external CI/CD tools and enabling the automation of many other development-related tasks. GitHub, the most popular social coding platform, has introduced GitHub Actions to automate workflows in its hosted software development repositories since November 2019. This chapter explores the ecosystems of development bots and GitHub Actions and their interconnection. It provides an extensive survey of the state-of-the-art in this domain, discusses the opportunities and threats that these ecosystems entail, and reports on the challenges and future perspectives for researchers as well as software practitioners.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection

    cs.CL 2025-07 reject novelty 1.0 of 10

    A legal document summarization framework is described, but the experiments use four non-legal summarization datasets and generic equations, so the claimed judicial efficiency improvements are not established.

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