This machine learning analyzer will review your code for constancy

Lack of consistency in the development of source code has made maintaining code over time and making updates more time-consuming and costly. Source{d}, the company enabling machine learning for large-scale code analysis, solves this long-standing problem with Machine Learning assisted code review.
 
The new source{d} Lookout analyzer, which has learned to model style based on experience from many code repositories, applies the model to the codebase being analyzed. In addition, source{d} Lookout understands that each codebase has its own nuances and learns to model the codebase style as precisely as possible, as opposed to relying on global style practices.
 
When new code is sent for review, source{d} Lookout analyzes it and detects any problems with style and automatically suggests fixes. By leveraging GitHub Suggested Changes, those suggestions can instantaneously be accepted (committed).
 
“Code reviews are one of the most essential practices for any software organization that cares about culture and quality. They provide quality control -- stopping defects from being introduced into a codebase at an early stage and they also give engineers the opportunity to share knowledge that would otherwise be isolated in a single team or, even worse, a single individual,” said source{d} VP of Product, Francesc Campoy. “Good code reviews take time, especially from the most senior engineers in an organization. That's why we're developing Machine Learning techniques that can not only help human reviewers automate the most boring and repetitive tasks but also the ones that simply require too much information for humans to handle effectively.”
 
Source{d} facilitates the digital transformation of companies through source code analysis and the adoption of modern developer tools for assisted code review. Learn more about the source{d} Lookout style analyzer and how you can benefit from it in your codebases. You can also check out the bot comments on the pull requests in this test repository on GitHub.