Versions Compared

Old Version 6

changes.mady.by.user Cathy Zhang

Saved on

compared with

New Version 7

changes.mady.by.user Cathy Zhang

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

Table of Contents

1. Problem

Currently there are many issues on Incubator-MXNet repo, labeling issues can help contributors who know a particular area to pick up the issue and help user. However, currently issues are all manually labelled, which is time consuming. And every time maintainers need to @ a committer to add labels. This bot will help automate/simplify this issue labeling process.

2. Goal

  • Part I - Email Bot
    Create weekly email todev@mxnet.incubator.apache.org:
    (Instead of sending emails directly to dev@, another option is to create another email alia and ask people who are interested in weekly reports to join. )Send daily GitHub issue reports to the mailing list:
    • Count of newly opened issues and closed issues in last 7 days
    • Average and worst response time for all new issues
    • List of non-responded new issues with links
    • List of non-responded issues outside SLA
  • Part II - Label Bot
    Create a bot to add labels for incubator-mxnet issuesPredict labels automatically for unlabeled issues
    • Send another version of daily GitHub issue reports to the mailing listCreate weekly email to internal team members:
      • Count of newly opened issues and closed issues in last 7 days
      • List of non-labelled responded issues
      • List of non-responded of unlabeled issues
      • Predictions of unlabeled issues
      • Pie chart with top 10 labels for all issues
      • Pie chart with top 10 labels for newly opened issues in last 7 days. （Add "unlabelled" as a segment）
      • A line/bar graph with week over week statistics of the number of issues closed and the number of issues opened
    • Generate a spreadsheet with detailed information of non-labelled issues. Every team member should have access to view and fill in labels to it.
    • Read filled-in labels and add labels to corresponding issue.
    • Build a web server which could response to GET/POST requests and realize self-maintenance:
      • Predict labels: once it receives GET/POST requests with issue ID, it will send predictions back.
      • Self-maintenance: it will re-train Machine Learning models every 24 hours.
  • Part III - Label Bot:
    This bot serves to help non-committers add labels to GitHub issues.
    • Recognize people's commands. ie "@mxnet-label-bot, please add labels :[A, B]". 
    • Be able to add labels for incubator-mxnet issues using a committer's credentials.
    Part III - Determine labels automatically from GitHub issues:
    • Identify the corresponding programming language to it (ex: Python, C/C++, Scala)
    • Multi-label classification

3. Approach

  • Part I - Email

    BotImage Removed

    Bot 

    An amazon cloudwatch event will trigger lambda function in a certain frequency(ex: 9am every Monday). Once the lambda function is executed, the issue report will be generated and sent to the mailing list. Figure1 shows the bot design and Figure2 shows demo email content
    .

    Image Added


Figure1 Email Bot Design




Figure 2 Demo Email Content


  • Part II -

    Label Bot

    Predict labels automatically for unlabeled issues


    Amazon cloudwatch event (a) will trigger lambda function(a) 9am every Monday. At that time, lambda function(a) will generate an email and write non-labelled issues' data into a Google sheet. Every team member has access to view and fill in labels to it. 12 hours later, another lambda function (lambda function b) will be executed and add labels to corresponding issues. This bot should have restricted permissions to avoid unexpected operations. Figure3 shows the bot design, Figure 4 shows the demo email content and Figure 5 shows the demo Google sheet content.

    Image Removed

Figure 3 Label Bot Design

Sample Issue Report

...

Image Removed







Figure 5 Demo Google Sheet Content

  • Part III Determine labels automatically


    Each instance can be assigned with multiple categories, so these types of problems are known as multi-label classification problem, where we have a set of target labels. Multi-label classification problems are very common in the real world, for example, audio categorization, image categorization, bioinformatics..etc. Our project mainly focus on text categorizations because labels are learned from issue title and issue description.

Steps to achieve it

Step 1: Retrieve Data
Extract data from GitHub issues into JSON format.

Step 2: Data Cleaning
Data cleaning is very important for us to keep the valuable information such as keywords extraction and reduce the noise.

Step 3: Vector Representation
Classifiers and learning algorithms cannot directly process the text documents in their original form. During a preprocessing step, the documents are converted into a more manageable representation. Typically, the documents are represented by feature vectors.

...

  • Problem Transformation
    • Binary Relevance
      This is the simplest technique, which basically treats each label as a separate single class classification problem.
    • Classifier Chains
      The first classifier is trained just on the input data and then each next classifier is trained on the input space and all the previous classifiers in the chain.
    • Label Powerset
      Transform the problem into a multi-class problem with one multi-class classifier is trained on all unique label combinations found in the training data.
  • Algorithm adaptation
    Manual:     rule-based
    Automatic:
    • Vector space model based
      • Prototype-based
      • K-nearest neighbor
      • Decision-tree
      • Neural Networks
      • Support Vector Machines
    • Probabilistic or generative model based
      • Naive Bayes classifier

4. Technical Challenges

  • Restrict permissions of this bot to avoid unexpected operations.
  • Training data is limited.

5. Reference

...