Why iCR


Generates accurate fixes not suggestions


Generates low false positive rate. ICR < 3% vs.others >50%


Increase code quality automatically at lower cost


Improves developer productivity by 11%


Focuses resources on features instead of bug fixing

What Drives iCR

iCR reduces to practice cutting-edge research done in three prominent disciplines.

Deep Static Analysis

Deep pointer analysis creates accurate knowledge vital to code repairing

Machine Intelligence

Machines learn from your code to generate fixes as if done by a human developer

Code Refactoring

Behavior enhancing refactoring transformations fix problems while preserving original behavior

How it works

Cloud Platform

  • Runs on AWS EC2 and Azure instances
  • Accesses source code securely using GitHub, GitLab or BitBucket.
  • Fixes turned into pull requests

Private Platform

  • Securely runs on your on-premise servers using Docker
  • Access source code from private repositories
  • Easy to use interface

What it fixes

71 Fixers of iCR for Java cover

  • Arithmetic Issues
  • Concurrency Issues
  • Improper Access Control
  • Logical Bugs
  • Null Pointer Issues
  • Weak Cryptography Issues

69 Fixers of iCR for Python cover

  • Arithmetic Issues
  • Improper Access Control
  • Logical Bugs
  • Security Misconfiguration Issues
  • Sensitive Data Exposure
  • Weak Cryptography Issues

44 Fixers of iCR for C cover

  • Buffer Overflow
  • Integer Arithmetic Issues
  • Logical Bugs
  • Memory Leak Issues
  • Null Pointer Issues
  • Security Misconfiguration Issues