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
Enterprise Use Cases
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.
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.
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.
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.
