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