The GitHub Development Workflow Automation Ecosystems
- URL: http://arxiv.org/abs/2305.04772v2
- Date: Wed, 17 May 2023 17:08:46 GMT
- Title: The GitHub Development Workflow Automation Ecosystems
- Authors: Mairieli Wessel, Tom Mens, Alexandre Decan, Pooya Rostami Mazrae
- Abstract summary: Large-scale software development has become a highly collaborative endeavour.
This chapter explores the ecosystems of development bots and GitHub Actions.
It provides an extensive survey of the state-of-the-art in this domain.
- Score: 47.818229204130596
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- OpenDevin: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
We introduce OpenDevin, a platform for the development of AI agents that interact with the world in similar ways to those of a human developer.
We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
arXiv Detail & Related papers (2024-07-23T17:50:43Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology [5.164094478488741]
AgileCoder is a multi agent system that integrates Agile Methodology (AM) into the framework.
This system assigns specific AM roles - such as Product Manager, Developer, and Tester to different agents, who then collaboratively develop software based on user inputs.
arXiv Detail & Related papers (2024-06-16T17:57:48Z) - Multi-Agent Software Development through Cross-Team Collaboration [30.88149502999973]
We introduce Cross-Team Collaboration (CTC), a scalable multi-team framework for software development.
CTC enables orchestrated teams to jointly propose various decisions and communicate with their insights.
Results show a notable increase in quality compared to state-of-the-art baselines.
arXiv Detail & Related papers (2024-06-13T10:18:36Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - The Software Genome Project: Venture to the Genomic Pathways of Open
Source Software and Its Applications [8.55939767653389]
textbfSoftware Genome Project is geared towards the secure monitoring and exploitation of open-source software.
Software Genome Project builds a complete set of software genome maps to help developers and managers gain a deeper understanding of software complexity and diversity.
arXiv Detail & Related papers (2023-11-16T13:18:24Z) - Intelligent Software Tooling for Improving Software Development [3.1763879286782966]
Deep Learning (DL) has shown huge advancements in automation across many domains, including Software Development processes.
One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces (GUIs) with RICO and ReDRAW to be trained on.
arXiv Detail & Related papers (2023-10-17T01:29:07Z) - Embedded Software Development with Digital Twins: Specific Requirements
for Small and Medium-Sized Enterprises [55.57032418885258]
Digital twins have the potential for cost-effective software development and maintenance strategies.
We interviewed SMEs about their current development processes.
First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach.
arXiv Detail & Related papers (2023-09-17T08:56:36Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - Workflow Automation for Cyber Physical System Development Processes [1.6735240552964108]
Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains.
We introduce a workflow modeling language for the automation of complex CPS development processes.
We implement a platform for execution of these models in the Assurance-based Learning-enabled CPS Toolchain.
arXiv Detail & Related papers (2020-04-12T17:32:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.