“CodeGuru observes your application, and every five minutes it creates a profile. The service does this through a machine-learning-powered profiler, one that requires a small agent to be embedded in the application. It also was designed to pinpoint “your most inefficient, unproductive, most expensive lines of code,” Jassy said. It can pinpoint resource leaks, atomicity violations, potential concurrency race conditions, unsanitized inputs, and wasted CPU cycles and the difficult-to-pinpoint on thread-safe classes, among other gotchas. The software itself grew from Amazon’s internal code review process, and draws from 10,000 application profiles on open source projects drawn from GitHub. When the dev commits the code, CloudGuru consults its models and algorithms provides an assessment of the code, in a human-readable form that pinpoints the problematic lines of code. The service is notable because it is one of the first ML-based code reviewing and profiling tool, said analyst Janakiram MSV, adding that it has potential appeal to developers when it is integrated with mainstream IDEs such as Visual Studio, VS Code, and IntelliJ (check back at The New Stack, as Janakiram will take a deeper dive into this service for us when he returns from the event).ĬodeGuru can be easily dropped into the development process: Add CodeGuru as one of the recipients to a pull request. CodeGuru can find many of the common, though tricky-to-notice, errors, and even highlight the inefficient (i.e. Company developers have to set aside time to review the code, and even the sharpest scrutiny can miss some elements.
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Manual code reviews are a pain, admitted AWS CEO Andy Jassy in a keynote at the conference, being held in Las Vegas this week. At the company’s annual user Re:Invent conference, the company has launched a new service, called CodeGuru, aimed to automate code reviews and provide application performance recommendations.
Amazon Web Services wants to bring the full power of machine learning (ML) to code development.
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