Tool Helps Automate Code Reviews 

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Among the IT plumbing being automated is the painstaking review of software code via machine learning tools designed to scan source code for inconsistencies while suggesting fixes that promise to speed the review process, thereby getting software updates out the door faster.

The Lookout tool released by code analysis specialist source{d} leverages machine learning to model coding “style” based on reviews of different code repositories. The trained model is then applied to the analysis of a particular code base. The company claims Lookout is trained to understand the nuances of different code bases and learns how to model them.

That approach differs from relying on standard style practices used for code reviews.

When style problems are detected, the quality control tool suggests code fixes based on the GitHub software development platform’s “Suggested Changes” offering. The public beta version of the GitHub tool was released in October 2018.

Using the GitHub tool, code reviewers can immediately accept, or “commit,” to changes.

Read the full story here at sister web site Datanami.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).