Look to these tools to improve your AI coding practices and the quality, security, and reliability of your AI-generated code.

Learn how to evaluate AI code quality platforms using enterprise criteria including scalability, predictive insights, and business impact.

Most organizations know they need to govern agentic output. Far fewer have a clear, practical path to doing so. Today, Sonar, a global leader in AI code verification, governance, and efficiency is ...

Architecture reviews often only consider the structure of the software. However, it is much more efficient and effective to ...

Shreyansh Sharma built high-performance financial data pipelines, improving accuracy, speed, scalability, and reliability for ...

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. For most of ...

In just its third edition, MEWA India has established itself as a leading platform for stakeholders across the nuts and dry ...

As AI reshapes how work gets done, organizations with strong process frameworks are best positioned to lead and maintain ...

The release includes an embedded MCP server that exposes Spring project analytics to AI coding assistants, along with first-class support for Spring AI and automated property refactoring.

When code is generated faster, quality, security and maintenance issues can also move through the pipeline more quickly, so ...

Attackers can inject indirect prompts in normal-looking repositories to trick Claude Code into spawning a reverse shell.

New benchmarks show semantic code graphs helping coding agents find change locations faster and complete updates more ...