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Code Analysis using Artificial Intelligence

 

Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we’ll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities

 

Find more critical issues and security vulnerabilities

Semantic Analysis unveils coding issues where the developer’s intent and syntax differ. This includes but goes beyond typical security and performance flaws.

Get results immediately

Semantic code analysis is lightning fast even on code-bases with millions of lines of code. You can use it in real-time as an extension in your IDE or directly in your Git workflow.

Benefit from a self-growing knowledge base

Instead of individual “rules” created by experts,Aicomsol learns from millions of open-source commits. This creates a constantly evolving base of all discovered bugs.

Learn from example fixes of real commits

We use AI to present logical conclusions What it means for you: We not only show you what is logically wrong, but also how others have fixed the same problem.

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