solutions
Browse our top use cases tailored to your needs and all the ways you can get more out of Sonar.
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Generative AI is trained using human code, at higher volumes and faster speeds, and its output includes all the same issues as human-created code. To maximize the advantages of generative AI in coding, developer teams need robust DevOps processes, reporting, and metrics that focus on code quality, security, and reliability.
Our use cases highlight the key areas your organization can tackle to achieve high quality, secure code at scale.
The landscape of software development is continually evolving, and at the forefront of this evolution is DevOps – a methodology that combines software development (Dev) with IT operations (Ops) to shorten the development lifecycle and provide continuous delivery with high software quality. However, challenges arise from inefficient processes to inadequacies in maintaining the balance between speed, quality, and security. The crux of solving these problems may lie in innovative tools and practices, notably Sonar static code analysis solutions.
Thought leadership pieces covering important facets of the developer industry.
Technical debt refers to the future costs of rework or maintenance that arise from prioritizing speed and short cuts over code quality in software development, with the debt accumulating over time and requiring resources to be paid off, making it crucial to address and minimize from the start of a project. Technical debt and its many facets can have a significant impact on software development lifecycles.
Core topics for developers focusing on code quality, security, and main tenets of the DevOps CI/CD workflow.
Resources and news
Our research team recently discovered an exploitable pattern in GitHub Actions that lets attackers exploit seemingly fixed vulnerabilities.
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This blog post looks at how seemingly small decisions can have massive effects, and the importance of prioritizing code quality to build reliable software.
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This release is not intended to compete with state-of-the-art (SOTA) reasoning models. Instead, it serves as a technical demonstration of how training data quality impacts the quality of a model’s code generation output.
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