Open Source Veterans Join source{d} as advisors
Today we’re thrilled to announce that Chris Aniszczyk, Vice President at the Linux Foundation, Jessie Frazelle, Open Source Engineer at Microsoft, Joseph Jacks, Founder at OSS Capital, Julien Barbier, CEO at Holberton School and Patrick Chanezon, member of Technical Staff at Docker, are all becoming advisors of source{d}.
“Combining code retrieval, language agnosticism and git history tools with familiar APIs parsing, source{d} Engine not only simplifies large scale code analysis but also lays the foundation for effective Machine Learning on Code and treating Code itself as a first-class analyzable data asset in the enterprise and beyond!” said Joseph Jacks.
In case you missed it, we recently announced the beta release of source{d} Engine, our solution to “Code as Data” challenges, which offers advanced code and architecture analysis to developers and engineering analytics and business intelligence to C-level executives. We also announced the Alpha release of source{d} Lookout, our first step towards a full suite of Machine Learning on Code (ML on Code) applications. It provides assisted code review on GitHub pull requests through code analyzers. We’re excited to develop both products in the open with the support of a growing community of contributors.
“The lack of standardized tools, metrics and methodologies for large scale source code analysis is real, that’s why I’m very excited to help source{d} on its mission to define de facto open source standards and collaboration opportunities for the Machine Learning on Code community” said Chris Aniszczyk.
One of the most important components of the source{d} Engine is its Universal Abstract Syntax Tree (UAST) which is a normalized form of a program’s AST, annotated with language agnostic roles and transformed with language agnostic concepts (e.g. Functions, Imports etc.). It has the potential to become a de facto standard for advanced static analysis of code and easy feature extraction for statistics or Machine Learning on Code.
“Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.” At a time when developer productivity is a key competitive differentiator in all industries, the source{d} team applied Fred Brook’s timeless wisdom to create a platform that treats Code as Data and enables Machine Learning on Code at scale, using the latest ML algorithms on a standardized data structure: I’m looking forward to a future where machines help every person and every organization on the planet to achieve more with code.” said Patrick Chanezon.
At source{d}, we are great believers in Open Source and its philosophy. Not only is our source code developed out in the open and made available to all, but also our culture, guides, and even OKRs are openly accessible on GitHub. We also curate a list of awesome blog posts, videos, research papers, datasets and software projects devoted to machine learning and source code and try our best to support the research community.
“source{d} is not only a great open source citizen with projects like source{d} Engine and their collection of research papers on Machine Learning for code, they also have the expertise and user experience skills to make something that will be truly revolutionary to the way developers interact with code,” said Jessie Frazelle.
We’re incredibly lucky to have Chris, Jessie, Joseph, Julien and Patrick as advisors as we release the first versions of our products. Similarly to what they did with the OCI, CNCF, Docker and Kubernetes communities, they will provide source{d} with engineering and product implementation insights, encourage collaboration between the different actors of the ML on Code community while helping us refine our open core business strategy.
