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As part of the code review process on GitHub, developers can leave comments on portions of the unified diff of a GitHub pull request. These comments are extremely valuable in facilitating technical discussion amongst developers, and in allowing developers to get feedback on their code submissions.

But what do code reviewers usually discuss in these comments?

In an effort to better understand code reviewing discussions, we’re going to create an SVM classifier to classify over 30 000 GitHub review comments based on the main code-related topic addressed by each comment (e.g. naming, readability, etc.).

Grab the Jupyter Notebook for this experiment on GitHub.

• Review Comment Classifications
• Loading The Data Set
• Preprocessing The Data
• Extracting Features
• Training The Classifier
• Classifying GitHub Review Comments
• Discussion
• Closing

Review Comment Classifications

The list of classifications we’re going to incorporate into our classifier are summarized in the table below. This list was developed based on a manual survey of approximately 2000 GitHub review comments I performed on randomly selected, but highly forked Java repositories on GitHub.

The selected categories reflect the most frequently occurring topics encountered in the surveyed review comments. Majority of the categories are related to code level concepts (e.g. variable naming, exception handling); however, certain review comments that did not naturally fall into any existing categories and were unrelated to the overall goal of code reviewing were placed in the “other” category.

In situations where a review comment discussed more than one subject, I gave it a classification according to the topic it spent the most words discussing.

Loading The Data Set

Now we’ll discuss our SVM text classifier implementation. This experiment represents a typical supervised learning classification exercise.

We’ll start by first loading our training data consisting of two files representing 2000 manually labeled comment-classification pairs. The first file contains a review comment on each line, while the second file contains manually determined classifications for each corresponding review comment on each line.

with open('review_comments.txt') as f:
    review_comments = f.readlines()
    
with open('review_comments_labels.txt') as g:
    classifications = g.readlines()
    

Integrations are what takes Slack from a normal online instant messaging and collaboration system to a solution that enables you to centralize all your notifications, from sales to tech support, social media and more, into one searchable place where your team can discuss and take action on each. In this article, I’ll share a simple bash script that reports local disk storage levels to Slack at a continuous time interval. It is easily deployable to multiple instances, highly configurable, and can helps teams take proactive measures in maintaining the operational well-being of their systems.

Download the App on GitHub

The script is available on GitHub and can be dropped anywhere on the instance you want to monitor. At a specified interval, it will post disk storage related information to slack as illustrated above. The drive information is retrieved using the df -h command on Unix systems. Additionally, listed drives on the system are color coded based on how much storage capacity they have left. Two quick steps are required for getting the integration setup and running.

1 - Create a Slack Webhook Notification:

This will allow the script to post as a bot/integration instead of as yourself (which would require your personal credentials). First, ensure the Incoming WebHooks app is installed in your slack organization. Next, click Add Configuration and read the instructions to configure the integration settings as desired. Copy the value for the Webhook URL field, which will be required in the next step.

2 - Use a time-based job scheduler to run the script:

The job scheduler will execute the script regularly at a time interval based on how often we want to view the reports. On a Linux environment, the crontab command, which is used to schedule commands to be executed periodically, is the perfect tool for the job. To create a new cronjob, simply type crontab -e in a command prompt. New jobs can be installed by adding a new entry to the file with the following syntax:

1 2 3 4 5 /path/to/command arg1 arg2