Machine learning driven issue classification bot. Add to your repository now!
git clone https://github.com/rafaelkallis/ticket-tagger ticket-tagger
cd ticket-tacker
npm install
npm run dataset
# run benchmark
npm run benchmark
# run linter
npm run lint
# run tests
npm test
# run server
npm start
For each experiment, we need a dataset that allows to test the stated hypothesis, as well as a baseline dataset which contains the same amount of labelled issues.
Does a repository specific dataset affect the model's performance?
# run baseline-issues benchmark
npm run dataset:vscode:baseline
npm run benchmark
# run vscode-issues benchmark
npm run dataset:vscode
npm run benchmark
Does a (spoken) language specific dataset affect the models perfomrnace?
# run baseline-issues benchmark
npm run dataset:english:baseline
npm run benchmark
# run english-issues benchmark
npm run dataset:english
npm run benchmark
Do code snippets affect the models perfomrnace?
# run baseline-issues benchmark
npm run dataset:nosnip:baseline
npm run benchmark
# run nosnip-issues benchmark
npm run dataset:nosnip
npm run benchmark
A dataset (with 10k bugs, 10k enhancements and 10k questions) can be downloaded using npm run dataset
.
The dataset was 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., add __label__
prefix to labels, etc.).
SELECT
label, CONCAT(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, '$.issue.body') AS body
FROM
[githubarchive:day.20180201],
[githubarchive:day.20180202],
[githubarchive:day.20180203],
[githubarchive:day.20180204],
[githubarchive:day.20180205]
WHERE
type = 'IssuesEvent'
AND JSON_EXTRACT_SCALAR(payload, '$.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=/path/to/cert.private-key.pem
GITHUB_SECRET=123456
GITHUB_APP_ID=123
PORT=3000
Note: When running app in production, environment variables should be provided by host.