Using a combination of conventional static code analysis, attention-based models, and a 360-degree Visualizer, Metabob is able to detect where problems are, how they interact with other aspects of your codebase, as well as offer plain-text recommendations on how to debug Python code.
Our model is written on open source code from Python developers across the globe, creating a dynamic, uniquely intuitive base to help identify where problems will likely occur. (Can you picture how many hours you’ll save debugging Python code?)
Instantly uncover the areas most likely to contain buggy code with Metabob. Install Metabob on your favorite code hosting platform and through static Python code analysis, it will point out which pieces of code contain errors and anomalies.
We do so in 4 simple steps:
Install the app on your Github, Gitlab, or Bitbucket account and add your repositories into Metabob.
After every code commit, you can go to the app and check out the status of code.
Metabob highlights all classes, methods and functions that may contain buggy code.
Metabob will help you resolve bugs and save you time by providing code optimization suggestions
Debug Python code in half the time with Metabob.
Connect to Metabob through Github or Bitbucket and our machine learning algorithm will point out which pieces of code contain bugs and performance sinks, helping you stop the tedious hunt for source code anomalies.
Forget combing through source code — the code visualization tool helps you understand big picture flows and granular code dependencies, so you can:
Your team can now understand unfamiliar code in minutes with macro and micro views of your software.
Quickly find what you’re looking for by searching modules, functions, methods, variables and more in the data flows.
