Machine learning for large scale code analysis startup, source{d}, launches public beta of its source{d} Engine and source{d} Lookout to speed software modernization

In a recent Thomson Reuters report all companies in traditional industries like finance, retail, manufacturing have become technology companies. While source code is now a large part of every company’s assets, that asset remains often underutilized. Large scale code analysis and Machine Learning on Code is the next logical step for companies as they progress on their digital transformation and IT automation journeys.

Founded in 2015 Eiso Kant, Jorge Schnura, and Philip von Have, source{d} is a Madrid, Spain-based Machine Learning for large scale code analysis startup building the first AI that understands code. The company is applying neural networks to source code from over 17 million software repositories and 6.6 million developers worldwide. Today, source{d} announced the launch of its public beta of source{d} Engine and public alpha of source{d} Lookout. Combining code retrieval, language agnostic parsing and git history tools with familiar APIs parsing, source{d} Engine simplifies code analysis. source{d} Lookout is a service for assisted code review that enables running custom code analyzers on GitHub pull requests.

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 world leading companies, source{d} develops projects transparently, collaborating with the broader community of Machine Learning on Code researchers. Headquartered in Madrid, with US offices in San Francisco, source{d} has raised $10 million from Otium, Sunstone Capital and others

source{d} powers Code as Data and Machine Learning on Code

source{d}, the only open-core company building a tech stack for Code as Data and Machine Learning on Code (ML on Code), turns code into an analyzable and productive asset across an enterprises source code repositories, facilitating the adoption of Inner Source practices at large traditional companies.

“With the right tools to retrieve and analyze all their code repositories, organizations can not only prevent quality and security issues but also streamline engineering efforts based on concrete metrics,” says Eiso Kant, source{d} CEO. “We envision every organization running a data pipeline over their software development life cycle, where source code becomes a unique, actionable dataset that can be analyzed and used in decision making and machine learning models.”

source{d} Engine offers advanced code and architecture analysis to developers and, for C-level executives, engineering analytics and business intelligence. Key features include Code Retrieval and Unification;, Language Agnostic Code Analysis, History Analysis and
Easy querying with familiar APIs.

“Combining code retrieval, language agnostic 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,” said Joseph Jacks, Aljabr CEO.  source{d} Lookout is the first step towards a full suite of Machine Learning on Code applications for assisted code review. Key features include: Language Agnostic Static Analysis and Inferred code style. source{d} helps companies modernize their codebases thanks to source code analysis and machine learning models.