Intel Open-Sources AI-Driven Code Error Detection Tool ‘ControlFlag’

The company says that it is excited to allow developers the opportunity to develop on it and see what more can be done using their “extremely valuable” and innovative technology.

Intel recently announced the public release of ControlFlag, its machine learning-based software that can detect problems in computer code. ControlFlag uses advanced self-supervised machine-learning (ML) techniques that enable it to autonomously detect coding anomalies, reducing the time used for debugging and improving the quality and integrity of systems.

ControlFlag is now open source and can be accessed at the Intel Labs Github page. The company says that it is excited to allow developers the opportunity to develop on it and see what more can be done using their “extremely valuable” and innovative technology.


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“We think ControlFlag is a powerful new tool that could dramatically reduce the time and money required to evaluate and debug code. According to studies, software developers spend approximately 50% of their time debugging. With ControlFlag and systems like it, I imagine a world where programmers spend notably less time debugging and more time on what I believe human programmers do best — expressing creative, new ideas to machines,” said Justin Gottschlich, Principal Scientist and Director/Founder of Machine Programming Research, Intel Labs. 

Image: Intel Labs

ControlFlag is a new AI that can work with any programming language and utilizes the emerging concept of semi-trust to learn from unlabeled source code. As more data becomes available, it evolves into what makes itself better by its own accord.

Since its introduction, ControlFlag has been tested on production-level software and widely used open-source software systems. Last year, ControlFlag identified a code anomaly in Client URL (cURL), a computer software project transferring data using various network protocols over one billion times a day. After reporting the anomaly to the cURL team, they agreed with ControlFlag’s findings and have subsequently patched their code. 

Recently, ControlFlag achieved state-of-the-art results by identifying hundreds of latent defects related to memory and potential system crash bugs in proprietary production-level software. In addition, ControlFlag found dozens of novel anomalies on several high-quality open-source software repositories. Each anomaly, thus far, has been acknowledged as a real defect by the open-source maintainers and has since been corrected.

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Victor Dey
Victor is an aspiring Data Scientist & is a Master of Science in Data Science & Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.

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