Code quality
How to choose your LLM without ruining your Java code
When evaluating a new AI model, ensuring the code compiles and executes is only the baseline. Experienced developers know that functionality is just the first step; the true standard for production-ready software is code that is reliable, maintainable, and secure.
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State of Code Developer Survey report: The current reality of AI coding
What we found challenges the common narrative. While AI adoption is massive, it hasn’t led to a simple, linear boost in productivity. Instead, it has shifted the bottleneck from writing code to verifying it.
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New data on code quality: GPT-5.2 high, Opus 4.5, Gemini 3, and more
Today, we are making all evaluations available in a new Sonar LLM leaderboard and sharing our latest findings on GPT-5.2 High, GPT-5.1 High, Gemini 3.0 Pro, Opus 4.5 Thinking, and Claude Sonnet 4.5.
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Why prioritizing code quality is the fastest way to reduce security risks
The common perception is that a security vulnerability is a rare, complex attack pattern. In reality, the journey of most flaws begins much earlier and much more simply: as a code quality issue. For both developers and security practitioners, understanding this lifecycle is crucial to building secure, reliable, and maintainable software.
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The inevitable rise of poor code quality in AI-accelerated codebases
The adoption of Large Language Models (LLMs) and AI coding assistants has radically accelerated the development lifecycle, offering the potential for developers to achieve up to a 55% increase in productivity and complete tasks twice as fast.
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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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