Machine learning driven issue classification bot. Add to your repository now!

Visit our GitHub App and install.

Ticket Tagger automatically predicts and labels issue types.
Copyright (C) 2018,2019,2020  Rafael Kallis

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>. 

• nodejs ^12.x is required to compile/install dependencies
• wget is required for fetching datasets
• we recommend at least 8 GB of RAM if you want to train or benchmark the model

git clone https://github.com/rafaelkallis/ticket-tagger ticket-tagger
cd ticket-tagger

# install appropriate nodejs version
npx nave use 12

# compile/install dependencies
npm install

# fetch dataset
npm run dataset

# run benchmark
npm run benchmark

# run linter
npm run lint

# run tests
npm test

# run server
NODE_ENV="development" npm start

Impact of Label Distribution

# balanced distribution
npm run dataset:balanced
npm run benchmark

# unbalanced distribution
npm run dataset:unbalanced
npm run benchmark

Impact of function words

npm run dataset:balanced
npm run benchmark

Impact of Language Consistency in Issue Tickets

# baseline
npm run dataset:english:baseline
npm run benchmark

# english
npm run dataset:english
npm run benchmark

Presence of Code Snippets in Issue Tickets

# baseline
npm run dataset:nosnip:baseline
npm run benchmark

# no snippets
npm run dataset:nosnip
npm run benchmark

Datasets can be downloaded either using npm run dataset:balanced or npm run dataset:unbalanced. The datasets were generated using github archive's which can be accessed through google BigQuery.

Add the query below to your BigQuery console and adjust if needed (e.g., resample issues to create a balanced dataset, etc.).

-- unbalanced dataset

SELECT
  CONCAT('__label__', label, ' ', title, ' ', REGEXP_REPLACE(body, '(\r|\n|\r\n)',' '))
FROM (
  SELECT
    LOWER(JSON_EXTRACT_SCALAR(payload, '$.issue.labels[0].name')) AS label,
    JSON_EXTRACT_SCALAR(payload, '$.issue.title') AS title,
    JSON_EXTRACT_SCALAR(payload, '$.issue.body') AS body
  FROM
    `githubarchive.day.201802*`
  WHERE
    _TABLE_SUFFIX BETWEEN '01' AND '10'
    AND type = 'IssuesEvent'
    AND JSON_EXTRACT_SCALAR(payload, '$.action') = 'closed' )
WHERE 
  (label = 'bug' OR label = 'enhancement' OR label = 'question')
  AND body != 'null';

You need a .env file in order to run the github app. The file should look like this:

GITHUB_CERT="<private key>"
GITHUB_SECRET=123456
GITHUB_APP_ID=123
PORT=3000

Note: When running app in production, environment variables should be provided by host.

• Building GitHub Apps
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