Intel has made its AI-powered bug checker open source. ControlFlag uses machine learning and works with any programming language with control structures.
The tool was developed by the Machine Programming Research (MPR) team at Intel Labs. The developers say it uses advanced, self-supervised machine learning techniques to detect coding anomalies.
It has now become open source and the team says they are:
“I am delighted to give developers the opportunity to develop on it and see what more can be done using this extremely valuable and innovative technology.”
Intel has tested ControlFlag on production software and widely used open source software systems, and last year identified a code anomaly in Client URL (cURL), a computer software project transferring data using various network protocols over a billion times per day. After reporting the anomaly to the cURL team, they accepted ControlFlag’s findings and subsequently corrected their code.
ControlFlag’s model anomaly detection system can be used for various problems such as detecting typographical errors or reporting a missing NULL check. The software has two main phases, a model exploration phase followed by a secondary phase during which the software searches for abnormal models.
The model exploration phase is a learning phase that extracts typical models into user-supplied GitHub repositories, and then builds a decision tree from the extracted models. The analysis phase then applies the extracted models to report abnormal expressions in user-specified target repositories.
In a document presented at the Proceedings of the 5th ACM SIGPLAN International Symposium on Machine Programming Describing Software, Niranjan Hasabnis and Justin Gottschlich of Intel Labs say that to their knowledge, the software is the first of its kind to detect a self-idiosyncratic programming pattern. supervised system. They also say that even though they only demonstrate ControlFlag for C / C ++, they designed it to be programming language agnostic. As such, it should be able to learn the idiosyncratic signatures of any type of control structure.
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