Hey, I'm the founder of Metadash.
Previously I was at Better.com and Glean AI.
My interests include design, tech, and startups.
I call New York City home.
:white_check_mark: Assign pull request reviewers intelligently.
Home Page: https://pull-review.herokuapp.com
License: MIT License
Hey, I'm the founder of Metadash.
Previously I was at Better.com and Glean AI.
My interests include design, tech, and startups.
I call New York City home.
Error: no_text
seems highly correlated with this
sometimes min author diversity will assign a fallback reviewer that doesn't have enough context on a piece of code - in the worst case, just one reviewer. to mitigate against that, the original reviewer shouldn't be replaced, but retained alongside the new reviewer.
not sure if min or max reviewers is ignored, but it's possible to "re-roll" review with someone already assigned
also could avoid sending the "joint" message in the originating room
good to have "foo, bar please review this pull request #hubot-review" since the string then identifies every PR that hubot-review has seen
for example, web/**
files should not be allowed to be reviewed outside of specific Slack rooms
should be clear it unassigns current assignees and ignores them for reassignment
for example:
hello [world] [some link here](#)
this will "find" a Slack link: hello <#|world>
the blame-derived reviewers will get re-assigned and re-pinged, while the random/fallback reviewers may change
TODO: consider delegating to OWNERS
if there are multiple rules with the same prefix:
backend: alice, bob
backend/foo: charlie, bob
backend/foo/bar: charlie
and the file name starts with backend/foo/bar, alice/bob will get matched first, instead of charlie
this should instead probably just blacklist the current assignees and let the planner come up with another set of valid reviewers, otherwise totally arbitrary assignments will be made
apparently the closing ] is ignored, should fix
this would listen for pull request comments like "/review" and run pull-review against the PR
make it clear in docs that order matters
in case Alice has changed more files than Bob, but has 1 files with almost zero ownership, Alice will be ranked lower than Bob, even if Bob has higher ownership in just one file
the issue is that the mapping function looks at only one of the real name or the username
Object.keys(rawUsers).forEach(function(userId) {
var user = rawUsers[userId];
if (user.real_name) {
if (chatUserMap[user.real_name]) {
robot.logger.error('User mapping already exists for ' + user.real_name);
} else {
chatUserMap[user.real_name] = userId;
}
}
if (user.name) {
if (chatUserMap[user.name]) {
robot.logger.error('User mapping already exists for ' + user.name);
} else {
chatUserMap[user.name] = userId;
}
}
});
this prevents long files from dominating a pull request's blame metrics, e.g.
File | Alice | Bob |
---|---|---|
1000_loc.txt | 400 | 505 |
100_loc.txt | 100 | 0 |
10_loc.txt | 5 | 0 |
Total LOC | 505 | 505 |
Avg % LOC | 63% | 17% |
here Bob equal to Alice on LOC changed, even though Alice has much higher average ownership
pull-review/src/get-reviewers.js
Line 106 in fea493c
dry run currently outputs the review plan in human-readable form, but could also output it in a machine readable form
Slack mentions no longer work for new users given https://api.slack.com/changelog/2017-09-the-one-about-usernames
possible alternatives: matching users by email, or matching users by name
key image is not being set with this PR body:
![screen shot 2017-03-02 at 4 58 15 pm](https://cloud.githubusercontent.com/assets/123456/23528971/b1a5fc50-ff69-11e6-81d8-3e78836c215a.png)
![screen shot 2017-03-02 at 4 58 30 pm](https://cloud.githubusercontent.com/assets/123456/23528972/b1b50132-ff69-11e6-96ac-ed09bb4e8083.png)
also fix the error display
consider gini coefficient
also add version
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Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
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China tencent open source team.