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This is the initial design of ML Based GitHub Bot. For more details, please refer: technical doc
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.
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- Multilabel Classification
- Solving Multi-Label Classification problems (Case studies)
- Text Categorization
- Doc2Vec
- Word2Vec
- Character-level CNN
- Multi-label classification on chest x-ray images
7. Design Upgrade of Label Bot
Issue:
There is a limitation with the current label bot implementation in that the current label bot can only label unlabelled issues. Key functionality to be implemented includes re-labelling labeled issues and streamlining the process of updating and removing labels. The current label bot implementation is also fairly inefficient in the way it automatically labels our issues and pull requests. The current design is based upon a pull model where every 5 minutes we trigger the bot to pull all issues/pull requests which we then label appropriately and consequently retrain our model every 24 hours. There is also a restriction which can be faced where GitHub limits users to make 5000 HTTP requests in an hour so we want to minimize the requests we make as much as possible.
Proposed Design Decision:
The efficiency of this bot can be improved if the bot was redesigned with a push model where as soon as an issue or pull request is made to the repository, we trigger the label bot to then appropriately label the issue. The lambda bot will also include functionality to not only add but also update, and delete labels.
Implementation:
Taking advantage of GitHub WebHook we can trigger the bot when an issue or pull request is made to the repository (which we specify by denoting the event that we want to subscribe to) this trigger is then managed by the lambda function which decides on the appropriate action to take on a GitHub label.
AWS Services: API Gateway handles receiving a POST notification from the GitHub WebHook and has that response be sent to our lambda which we use to send to SQS. SQS handles management of multiple messages which are received and then sends this data to our lambda bot. The lambda bot reads the payload that has been received from SQS and takes the appropriate action onto a GitHub label.
Current Proposed Design Implementation:
Usage:
Add functionality: adds labels specified to the list of labels:
@mxnet-label-bot add [label1, label2]
Remove functionality removes labels specified from the list of labels:
@mxnet-label-bot remove [label1, label2]
Update functionality updates the labels of the issue to only the labels specified in the list:
@mxnet-label-bot update [label1, label2]