Code debugging 2023-05-06
Metabob icon


Improve software security through code review.
Metabob website
Code debugging#30 most recent
codedebugprogrammingFree + from $20/mo
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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.


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Jun 7, 2023
Great tool. Easy to use. Provides great results!

Feature requests

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Metabob was manually vetted by our editorial team and was first featured on June 2nd 2023.
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37 alternatives to Metabob for Code debugging

Pros and Cons


Context-sensitive code recommendations
Enforces code quality
Provides project metrics
Improves team productivity
Can be customized
On-premises deployment
Prevents known security vulnerabilities
Compliant with software security standards
Identifies and learns bug causes
Detects context-based problems
Trained on millions of bug fixes
Replaces several traditional tools
Integrated with security gateways
Works for teams and enterprises
Increases developer productivity
Detects critical errors early
Supports diverse programming languages
Uses graph neural networks
Utilizes large language models
Minimal false positive rate
Secrets scanning feature
Automated code fix recommendations
Offers refactoring recommendations
Reducing technical debt
Optimizes Line of Code performance
Overall code quality metrics
Developer-based code quality metrics
Most frequent problems metrics
Estimated task completion time
Save debugging time
Supports Github, Bitbucket, Gitlab
Available on VS Code
Generates problem explanation and resolution
Low standard debugging time
Analyzes complete code bases
Targets most relevant team problems
High detection rate of errors
Improves code maintainability
Identifies software security vulnerabilities
User-friendly interface
Quick setup without CI
Software security scanning
Provides actionable key performance metrics
Proudly partnering with organizations


Limited language support
False positives despite low rate
Requires integration with code repositories
On-premise deployment complexities
Might overlook non-traditional bugs
Potential privacy issues with code data
Inflexible with non-standard coding practices
Dependent on bug-fix data accuracy
Limited to VS Code extension


What is Metabob?
How does Metabob improve software security?
How does Metabob's generative AI and graph-attention networks work?
What kind of coding issues can Metabob detect?
How does Metabob learn to detect and fix code problems?
What are some code quality insights provided by Metabob?
Can Metabob be customized to meet the needs of a specific team?
How does Metabob compare to other static code analysis tools?
How does Metabob assist in preventing known security vulnerabilities?
How is Metabob compliant with software security industry standards?
How does Metabob increase developer productivity?
How does Metabob aid in detecting critical errors early in the development process?
What programming languages are supported by Metabob?
Is Metabob available for VS Code?
Can Metabob be deployed on-premises?
How does Metabob facilitate code reviews and improve software security?
Why is Metabob better than traditional static code analysis tools?
What are the refactoring recommendations provided by Metabob?
Does Metabob provide project metrics and insights into team productivity?
How does Metabob detect and resolve software bugs and security vulnerabilities?

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