iCR for Python User Guides
iCR for Python 3.0.2
iCR for Python 3.0.2
  • Table of contents
    • Introduction
    • Overview
    • Getting Started
      • Installing iCR for Python
      • Managing your service
        • Opening Ports
      • Authorizing Access to Your Source Code
        • Authenticating GitHub Access with a Cloud-Based VCS Repository Service
          • Authenticating GitHub Access with a Private VCS Repository
        • Authenticating GitLab Access with a Cloud-Based VCS Repository
          • Authenticating GitLab Access with a Private VCS Repository
        • Authenticating Bitbucket Access with a Cloud-Based VCS Repository
          • Authenticating Bitbucket Access with a Private VCS Repository
          • Setting Bitbucket Server Credentials in the Navigator
    • Using the Navigator
      • Connecting to the Navigator
      • Setting your private passphrase
      • The Navigator top banner
      • The Analysis Engine status
      • Selecting Your Source Code
        • Using a cloud-based VCS
        • Selecting your branch
        • Using a private VCS
        • Using a local project
        • Setting the scope of your analysis
      • Integrating with your bug tracking system
        • Integrating with Jira - Define Your Project
        • Integrating with Jira - Authorizing Access for iCR
        • Integrating with Jira - Connecting with iCR
    • Using the Analysis Engine
      • Initiating an analysis
      • Monitoring the analysis
      • Interrupting the analysis
    • Reviewing your results
      • Reviewer summary and filters
      • Filter by Directory pane
      • Filter by Category pane
      • Reviewing a fix
      • Accepting a fix
        • Accepting a fix when integrated with your bug system
      • Rejecting a fix
        • Rejecting a fix when integrated with your bug system
      • Undoing a fix
        • Undoing a fix when integrated with your bug system
      • Rejected fix history
      • Providing feedback
      • Applying the fixes
      • Cases needing manual attention
      • Capturing results for printing or sharing
      • Ending a reviewer session
    • When you are complete
    • Appendix – List of supported fixers
    • Appendix – Example Summary Report
    • Appendix - Sample Bug Listing
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  1. Table of contents

Using the Analysis Engine

iCR for Python supports the analysis of projects being managed by Cloud-Based services such as GitHub, GitLab or Bitbucket as well as locally accessible projects. For the purposes of demonstrating the Analysis Engine and the Reviewer, we will use examples using the cloud-based repositories. The behavior when using locally accessible projects is nearly identical and should be easy to infer from the following descriptions.

The Analysis Engine works on your Python source code by focusing only on executable lines of code. It does process some of the comments but the Analysis Engine does not count blank lines or most comments in its OpenRefactory Bundled Lines of Code (OBLoC) count. You can use the cloc Linux command to get an estimate of how many executable lines of code are in your project.

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Last updated 1 year ago