Accelerate development by swiftly resolving issues detected by SonarQube’s precise static code analysis with one-click AI-powered recommendations. Empower teams with contextual, LLM‑driven suggestions that reduce manual debugging and keep developers in full control.
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AI CodeFix is a powerful capability that suggests code fixes for issues discovered by our static code analysis solutions SonarQube Server and SonarQube Cloud. By automating the resolution of common coding problems, AI CodeFix significantly boosts developer speed and productivity. It delivers contextual, LLM‑driven recommendations that teams can review, adapt, and apply with confidence to maintain quality and oversight.
AI CodeFix automatically generates code fix suggestions with a click, minimizing manual debugging efforts and allowing developers to focus on more critical tasks. It leverages contextual understanding to propose targeted changes aligned with rule intent. Suggestions are presented transparently so teams can review diffs, validate impact, and apply only what meets their standards. Over time, feedback and model improvements enhance recommendation quality, further streamlining remediation workflows.
Streamline issue resolution to improve developer productivity and reduce time-to-market. Maintain oversight with transparent suggestions and apply only the changes that meet your standards.
Developers can focus on creative problem-solving instead of tedious error correction. Context-aware AI suggestions reduce cognitive load, accelerating delivery while preserving full oversight.
Review and apply suggested fixes only when they meet your needs, so you stay in charge. Transparent diffs and explanations support informed decisions while preserving quality and consistency.
Debugging is the process of finding and fixing bugs, defects, and errors in a computer program by identifying the root cause across source code, program execution, and data flow. AI CodeFix complements these techniques by reducing manual analysis and accelerating remediation through static code analysis and automated, context-aware fixes.
Artificial intelligence fits into how different roles work, without forcing new habits. It delivers explainable suggestions directly in IDEs, pull requests, and CI/CD pipeline checks so teams can review, apply, and audit changes within their existing flow.
Review diffs in the IDE, rerun checks, and commit faster with clear rationales for each change.
Enforce consistency with explainable patches in PRs, preserving control and traceability.
Guide adoption and governance with project‑level policies, model selection, and reporting on impact.
Focus on critical rules and hotspots; use transparent diffs to validate remediations before merge.
Modern software development involves finding and fixing many types of errors across source code, program execution, and evolving codebases. AI CodeFix builds on static code analysis to automatically suggest fixes for the most common issues that impact code quality and security, helping developers resolve problems faster and with greater confidence.
Syntax errors occur when code does not follow the rules of a programming language. AI CodeFix detects these issues early through static code analysis and suggests accurate corrections. This helps developers avoid build failures and maintain smooth program execution.
Bugs are logic errors that cause unexpected or incorrect behavior in a computer program. AI CodeFix analyzes control flow and data flow to identify the root cause of these issues. It then proposes targeted fixes that resolve the problem without introducing new defects.
Defects often emerge from edge cases, incorrect assumptions, or complex interactions between components. AI CodeFix helps isolate these problems by examining source code patterns and execution paths. Suggested fixes improve reliability and reduce recurring errors over time.
Security vulnerabilities expose software to potential attacks and misuse. AI CodeFix recommends secure coding fixes based on established best practices to mitigate these risks. Developers remain in control by reviewing and applying changes that strengthen code quality and security.
Code smells indicate maintainability issues that can slow down software development and debugging. AI CodeFix highlights inefficient or unclear patterns and suggests cleaner, more readable alternatives. These improvements help keep codebases easier to understand and evolve.
Security hotspots flag sensitive code that requires careful human review rather than automatic changes. AI CodeFix provides contextual guidance and suggested remediations to support informed decisions. This helps teams balance automation with responsible, security-aware development.
SonarQube est spécialement conçu pour DevOps. Il intègre une analyse automatisée du code directement dans votre pipeline et prend en charge les langages de programmation déjà utilisés par vos équipes.
"Sonar helps our development team confidently make both AI-assisted and human-developed code fit for production by reviewing and establishing rules of good programming practices to achieve better code."
Dario Flores, Technical Quality Specialist
Dario Flores, Technical Quality Specialist
"Sonar helps our development team confidently make both AI-assisted and human-developed code fit for production by reviewing and establishing rules of good programming practices to achieve better code."
Intégrez SonarQube à votre flux de travail et commencez dès aujourd'hui à détecter les vulnérabilités.
4.6 / 5
Sonar AI CodeFix is an innovative capability embedded inside Sonar’s static code analysis platforms, including SonarQube Server and SonarQube Cloud. It automatically suggests code fixes for issues discovered by Sonar’s analysis tools, helping developers remediate bugs, vulnerabilities, and code quality problems with minimal effort and maximum speed. This AI-powered programming assistant streamlines workflows, boosts productivity, and reduces time-to-market for teams of all sizes.
By automating the resolution of common coding problems, Sonar AI CodeFix lessens the cognitive load on developers, allowing them to focus on creative problem-solving rather than tedious manual error correction. Its contextual understanding ensures every suggestion fits production requirements and upholds best programming practices, resulting in consistently secure, high-quality code.
