Metabob is an AI tool that uses generative AI and graph-attention networks to facilitate code review and improve software security. The tool detects, explains, and fixes coding issues created by humans and AI.
It can detect and classify hundreds of contextual code problems, including those that traditional static code analysis tools cannot detect. Metabob’s AI is trained on millions of bug fixes performed by experienced developers, allowing it to learn the root causes of many context-based problems.
Metabob generates context-sensitive code recommendations for bugs and code smells, enforces code quality and best practices with refactoring recommendations, and provides insights into project metrics and team productivity.
Additionally, the tool can be deployed on-premises and customized to detect the most relevant problems for a specific team. Metabob replaces several traditional static code analysis tools such as SonarQube, Deepsource, Code Climate, Codacy, Checkmarx, Snyk Code, Veracode, Semgrep, and WhiteSource.
The tool integrates with security gateways to prevent known security vulnerabilities before merging, making it compliant with software security industry standards such as SANS/CWE top 25, OWASP top 10, and MITRE CWE.
Metabob outperforms traditional static code analysis tools such as Sonarqube and linters, increasing developer productivity and detecting critical errors earlier in the development process.
The tool can identify and learn the root causes of software bugs and software security vulnerabilities, providing actionable development productivity and code quality key performance metrics.
More details about Metabob
How does Metabob enhance developer productivity?
Metabob boosts developer productivity by offering context-sensitive code recommendations tailored for identified bugs and code smells. It streamlines debugging processes by automatically generating suggestions for code fixes and promotes code quality through refactoring recommendations.
What types of refactoring suggestions does Metabob provide?
Metabob’s AI offers refactoring suggestions aimed at improving areas with disorganized or inefficient code. These recommendations help prevent technical debt accumulation and optimize the performance of code segments.
How does Metabob learn to identify and resolve code issues?
Metabob’s AI undergoes training using extensive datasets of bug fixes performed by seasoned developers. This training equips it to comprehend the underlying causes of various context-specific issues, continually enhancing its ability to detect and resolve code problems effectively.
What insights does Metabob provide on code quality?
Metabob provides insights into various aspects of code quality, including overall metrics, individual developer contributions to code quality, common issues categorized within the codebase, and estimated timeframes for task completion.
