Metabob

Improve software security through code review.

code debug programming

Tool Information

Primary Task	Code debugging
Category	technology-and-development
Pricing	Free + from $20/mo
Country	United States

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.

Metabob is an artificial intelligence company based in Mountain View, California, founded in 2021. It specializes in code review and debugging for the software development industry. The company employs advanced AI technologies, including graph neural networks and large language models, to enhance code quality and security.

Metabob offers a range of services designed to improve software development processes. These include AI code review, static code analysis, debugging and refactoring, and security scanning. The platform functions as an ensemble AI system that classifies, identifies, and explains non-deterministic faults in source code.

The team at Metabob consists of experts in AI, natural language processing, and machine learning, with leadership experienced in deep tech startups and entrepreneurial marketing. The company has secured $480,000 in funding from various investors, including the National Science Foundation and NetApp Excellerator.

Pros
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

Cons
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

Frequently Asked Questions

1. What is Metabob?

Metabob is an AI tool that leverages generative AI and graph-attention networks to conduct code reviews and enhance software security. It detects, explains, and repairs coding issues generated by humans and AI. Additionally, Metabob can recognize and categorize hundreds of contextual code problems which traditional static code analysis tools might miss.

2. How does Metabob improve software security?

Metabob improves software security by detecting and explaining code problems, and then suggesting fixes. It can prevent known security vulnerabilities from being merged into the main codebase. Metabob is also compliant with major software security industry standards such as SANS/CWE top 25, OWASP top 10, and MITRE CWE.

3. How does Metabob's generative AI and graph-attention networks work?

Metabob uses a proprietary Graph Neural Network that employs an attention mechanism to comprehend both semantic and relational markers for a thorough representation of the input. Once a problematic code is detected and classified, the data is stored in Metabob's backend. A Large Language Model subsequently uses the stored information to generate a context-sensitive problem explanation and resolution.

4. What kind of coding issues can Metabob detect?

Metabob can detect and classify hundreds of contextual code problems, ranging from race conditions to unmanaged edge cases. These include issues that traditional static code analysis tools might overlook.

5. How does Metabob learn to detect and fix code problems?

Metabob's AI is trained on millions of bug fixes that were completed by experienced developers. This training enables it to understand the root causes of many context-based problems, continually improving its ability to detect and fix code issues.

6. What are some code quality insights provided by Metabob?

Metabob offers insights into metrics like overall code quality, code quality based on individual developers, the most frequent problems in a codebase by category, and the estimated time to complete tasks.

7. Can Metabob be customized to meet the needs of a specific team?

Yes, Metabob can be adjusted to meet the unique needs of a specific team. It can be deployed on-premises on a company's private cloud and tailored to detect problems that are most relevant to the team.

8. How does Metabob compare to other static code analysis tools?

Metabob outperforms traditional static code analysis tools such as SonarQube and linters by utilizing generative AI. This approach helps detect a higher rate of critical errors and increases developer productivity by providing targeted and actionable solutions.

9. How does Metabob assist in preventing known security vulnerabilities?

Metabob scans the code for known security vulnerabilities and prevents them from being integrated into the primary codebase. This preemptive approach allows problems to be addressed early, enhancing overall software security.

10. How is Metabob compliant with software security industry standards?

Metabob complies with software security industry standards such as SANS/CWE top 25, OWASP top 10, and MITRE CWE. Compliance is achieved through its ability to prevent known security vulnerabilities before they are merged into the main codebase.

11. How does Metabob increase developer productivity?

Metabob increases developer productivity by providing context-sensitive code recommendations for detected bugs and code smells. It facilitates efficient debugging by auto-generating code fix recommendations and enforces code quality with refactoring suggestions.

12. How does Metabob aid in detecting critical errors early in the development process?

Metabob aids in detecting critical errors early in the development process by analyzing the whole codebase. It uses generative AI to facilitate code reviews, detect the root causes of software bugs and software security vulnerabilities, and provides actionable development productivity enhancements and code quality-based key performance metrics.

13. What programming languages are supported by Metabob?

Metabob supports several programming languages including Python, JavaScript, TypeScript, C++, C, and Java.

14. Is Metabob available for VS Code?

Yes, Metabob is available for Visual Studio Code.

15. Can Metabob be deployed on-premises?

Yes, Metabob can be deployed on-premises on your organization's private cloud.

16. How does Metabob facilitate code reviews and improve software security?

Metabob uses generative AI to automate the code review process, which improves software security by detecting, explaining, and fixing coding problems. Preventing known security vulnerabilities before merging further enhances software security.

17. Why is Metabob better than traditional static code analysis tools?

Metabob is superior to traditional static code analysis tools because it utilizes a combination of Graph Neural Networks and Large Language Models to better detect and classify hundreds of contextual code problems. It is also able to provide context-sensitive explanations and resolutions for these problems.

18. What are the refactoring recommendations provided by Metabob?

Metabob's AI provides refactoring recommendations for areas with disorganized and inefficient code. These suggestions aim to prevent the creation of technical debt and optimize the performance of lines of code.

19. Does Metabob provide project metrics and insights into team productivity?

Yes, Metabob provides actionable insights about a project's code quality and reliability, along with a bird's eye view of team productivity. It provides key metrics such as overall code quality, code quality on a developer basis, most frequent problems in a codebase by category, and estimated time to complete tasks.

20. How does Metabob detect and resolve software bugs and security vulnerabilities?

Metabob utilizes its trained AI to identify and understand the root causes of software bugs and security vulnerabilities. It uses Graph Neural Networks for problem detection and classification, and feeds this information into a Large Language Model which generates a context-sensitive explanation and resolution for the issue.