SAN FRANCISCO, Sept. 19, 2018 (GLOBE NEWSWIRE) -- Today source{d}, the company enabling Machine Learning for large scale code analysis, announced the 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.

According to a recent Thomson Reuters report, by now all established 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.

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 - Retrieve and store the code history of the organization (both open-source and private repositories) as a dataset.
• Language Agnostic Code Analysis - Automatically identify languages, parse programs, and extract the pieces that matter in a completely language-agnostic way.
• History Analysis - Analyze not only the current version of your code base but also its evolution over time and individual contributions to each repository.
• Easy querying with familiar APIs - Use Standard SQL to obtain insights from your source code and version control history, while classifying languages and parsing code through simple custom functions.

"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: code quality analyzers that are independent of the language they are written in.
• Inferred code style: Recognize source code patterns related to programming style from a given organization or particular project and warn developers about inconsistencies related to style in the code being contributed.

source{d} helps companies modernize their codebases thanks to source code analysis and machine learning models.

If you are interested in testing these products click 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 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. To learn more, visit sourced.tech.

Editorial Contact:
Joseph Eckert for source{d}
jeckert@eckertcomms.com