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Metabob

AI-powered code review for improved security

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June 2, 2023

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Metabob

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AI-powered code review for improved security

Overview

Overview Releases Pricing Reviews Prompts Pros & Cons Q&A Alternatives Recommendations

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Code Review Software Security AI For Coding Programming Coding Standards Code QualityFree + 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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Initial release

May 6, 2023

Initial release of Metabob.

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Pricing

Pricing model

Freemium

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$20/month

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Monthly

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Reviews

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Comments(1)

Fabian Eggers

🙏 6 karma

Jun 7, 2023

Rated it

Great tool. Easy to use. Provides great results!

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Metabob was manually vetted by our editorial team and was first featured on June 2nd 2023.

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Really handy tool — just double-click and it drops in things like videos, FAQs, or product blocks. Makes blog posts way more engaging without any extra hassle.

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Pros and Cons

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

View 39 more pros

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

View 4 more cons

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Q&A

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.

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.

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.

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.

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.

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.

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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.

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.

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.

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.

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.

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.

What programming languages are supported by Metabob?

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

Is Metabob available for VS Code?

Yes, Metabob is available for Visual Studio Code.

Can Metabob be deployed on-premises?

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

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.

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.

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.

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.

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.

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