SEATTLE, Dec. 11, 2018 (GLOBE NEWSWIRE) -- KubeCon -- As the Kubernetes project nears 2 million lines of code (including all languages and generated files), the 4-year-old open source project is showing many signs of maturity, according to an analysis by source{d}, the company enabling Machine Learning for large-scale code analysis.
The velocity of commits for the core Kubernetes project seems to be slowing down as the community focus moves to infrastructure testing, cluster federation, Machine Learning and HPC (High Performance Computing) workloads management. With just under 16,000 methods, the Kubernetes API also seems to be stabilizing despite its high level of complexity.
This analysis leverages source{d} Engine to retrieve and analyze all the Kubernetes git repositories through SQL queries to get insights into the project codebase history, as well as emerging trends.
“Kubernetes has clearly evolved from one of the most active open source projects of all time to a production-ready platform for the enterprise,” said Francesc Campoy, vice president of product and developer relations at source{d}. “These source{d} Engine queries have revealed useful insights into the relatively young Kubernetes project; imagine the possibilities for large companies which have very heterogeneous and old codebases.”
The source{d} analysis includes:
Here are some details on findings of the analysis:
source{d} Engine turns code into an analyzable and productive asset across large codebases, facilitating the digital transformation of large, traditional, companies through software modernization and the adoption of Inner Source practices.
Full copies of the report can be downloaded here. Companies interested in getting their own code base analyzed can request an analysis here.
About source{d}
source{d}, the only open core company to turn code into actionable data and business intelligence, is building the tech stack that enables large-scale code analysis and machine learning on code. Used by top engineers at the world’s leading companies, source{d} develops projects transparently, collaborating with the broader community of Machine Learning on Code researchers. Headquartered in Madrid, with a U.S. office in San Francisco, source{d} has raised $10 million from Otium, Sunstone Capital and others. To learn more, visit sourced.tech.
Editorial Contact:
Joseph Eckert for source{d}
jeckert@eckertcomms.com
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