Sonar's latest blog posts
Announcing SonarSweep: Improving training data quality for coding LLMs
Recent research from Anthropic has shown that even a small amount of malicious or poor quality training data can have a massively negative impact on a model’s performance, exposing users to significant security and quality issues.
Quality assurance in the AI era: a leadership imperative, according to S&P Global Market Intelligence
In the rapidly evolving AI era, technology leaders are facing a fundamental shift in how code is created, validated, and governed.
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Analysis evidence from SonarQube now available in JFrog AppTrust
By integrating SonarQube's industry-leading automated code review with JFrog's new AppTrust governance platform, together we are providing the essential framework for software engineering teams to embrace AI-driven speed without compromising on control.
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Deploying SonarQube on Kubernetes with Helm Charts
By using a Helm Chart to deploy SonarQube Server, teams can quickly provision a production-ready SonarQube Server instance with minimal configuration while adopting best practices for scalability, security, and maintainability.
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How reasoning impacts LLM coding models
This report provides a deep dive into GPT-5’s four reasoning modes—minimal, low, medium, and high—to understand the impact of increased reasoning on functional correctness, code quality, security, and cost.
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Diving into the 3 traits that define your LLM’s coding personality
Our recent “State of code” report moved beyond traditional benchmarks to understand the full mosaic of an LLM's capabilities. The research revealed that while leading models share common strengths and flaws, each has a unique style.
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The Coding Personalities of Leading LLMs—GPT-5 Update
GPT-5’s arrival on the scene adds an important new dimension to the landscape, so we have updated our analysis to include it.
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The Coding Personalities of Leading LLMs
Make smarter AI adoption decisions with Sonar's latest report in The State of Code series. Explore the habits, blind spots, and archetypes of the top five LLMs to uncover the critical risks each brings to your codebase.
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Securing Go Applications With SonarQube: Real-World Examples
Take a deep dive into some vulnerabilities in Go applications and understand how SonarQube Cloud helps developers detect and mitigate them during the development cycle.
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SonarQube IDE: Announcing support for AI-Native IDEs
As development velocity accelerates, so does the potential for introducing subtle bugs and new security vulnerabilities.
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Java24: Go deeper on parsing Java class files and broader with Stream gatherers
Version 24 version introduces several new language features which collectively simplify code, and provide powerful tools for bytecode manipulation and advanced stream processing.
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Sonar's Take: Software Development Under America's AI Action Plan
The White House's "America's AI Action Plan" aims to accelerate innovation, but for software development, speed must not compromise security. Nathan Jones, VP of Public Sector at Sonar, explores the recently published plan, risks of AI-generated code, and explains how static analysis tools help ensure AI adoption is both fast and secure.
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