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Review legacy codebases with AI

Metabob helps companies to tackle technical debt by debugging and refactoring large codebases

Reviewing code with Graph Neural Networks

Metabob’s technology is based on Graph Neural Networks (GNNs) and is capable of understanding code logic and context of large codebases.

LLMs

Deep learning models that use billions of parameters and an attention mechanism to predict the most likely token to follow a given input

Made to generate not analyze

Unable to connect problems to relevant contextual information

Require human input to detect problems across the codebase

GNNs

Models that utilize an attention mechanism to comprehend both semantic and relational markers, resulting in a holistic representation of the output

Ability to analyze a full codebase & examine relationships between different sections

Capability to effectively connect problems in code to their relevant context

Understands the impact of different problems to the codebase as a whole

Trusted by developers at

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Enterprise Use Cases

AI Static Code Analysis

Debug & refactor legacy code

Metabob is a vital tool for organizations grappling with expansive and complex legacy codebases, which makes detecting potential bugs and suggesting fixes a resource-draining problem.

Metabob Graph Neural Network allows it to analyze the complete codebase and understand the structure and data flow of the analyzed application, turning legacy code maintenance into a manageable task.

Problem detection with AI static code analysis

Review and detect issues in new code

By analyzing new code in real time, Metabob helps identify potential bugs early in the development process, thereby saving development time, improving software quality, and preventing costly post-deployment fixes.

Metabob can be integrated into developers' IDEs and your company's CI/CD pipeline to automatically perform code reviews on new code.

Validate correctness & cohesion of AI-generated code

As a sophisticated analysis tool, Metabob can scan AI-generated code for bugs, validate cohesion with the rest of the project and suggest corrections, acting as a crucial feedback loop that helps AI code generation models improve over time.

Use Case Customization

When deployed on-premise, Metabob can be tuned to your organization's specific use case by allowing it to understand which problems matter the most to you.

Metabob can be tuned by enforcing specific detection categories relevant to your use case, or by utilizing your organization's existing commit & bug fix history.

Competitive advantage

Metabob's underlying technique sets it apart from competition

Features

LLM-based tools

(CodeRabbit, Korbit AI, CopilotChat)

Rules-based tools

(Sonar, DeepSource, Coverity, etc.)

Analysis of complete codebases with a strong contextual understanding/retention

Yes

No

No

Detects compile-time errors

Yes

Yes

Yes

Detection rate of run-time errors

High

Medium*

N/A

Accuracy of context-sensitive problem descriptions and resolutions

High

Medium

No

Adaptability to customers' specific use cases

Yes

Yes

No

Problem detection requires human input

No

Yes

Yes

*depending on the amount of input data

Hear what our customers say

Don't take our word for it, take theirs.

The software has proven to be intuitive, robust, and highly effective in meeting our needs. It has significantly contributed to improving our code quality and increased the productivity of our software developers.

Ryo Kaneko
Director of Innovations, NEC X

Metabob has significantly enhanced our code quality and improved collaboration among our
engineers. Its AI-driven recommendations have reduced debugging time by 80%, allowing us
to focus on innovation while maintaining reliable and efficient code.

Fred Qi
CEO, Dereka AI

Our codebase needs a lot of love, it is so messy and large that we cannot address it with human eyes. We have plenty of unhandled edge cases and errors, which can result in problems for our end users. This is why we need Metabob's technology.

Anonymous
Head of Software Engineering, Fintech Enterprise

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