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This will give us more visibility into the results to make sure it's working as expected and to fine tune the logic if we want.
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This pull request enhances the reviewer assignment script by tracking and logging the raw counts of review-requested and authored PRs per reviewer, and updating the selection logic to sort and print all candidate reviewers. The feedback suggests optimizing the sorting key by precomputing reviewer indices into a dictionary to avoid O(N) lookups during sorting.
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| ranked = sorted( | ||
| eligible, | ||
| key=lambda r: ( | ||
| round(scores[r], 6), | ||
| last_assigned[r], | ||
| reviewers.index(r), | ||
| ), | ||
| ) |
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Calling reviewers.index(r) inside the sorting key function results in an O(N) lookup for each comparison. Although the list of reviewers is small, it is more efficient and idiomatic to precompute the reviewer indices into a dictionary for O(1) lookups during sorting.
Suggested change
| ranked = sorted( | |
| eligible, | |
| key=lambda r: ( | |
| round(scores[r], 6), | |
| last_assigned[r], | |
| reviewers.index(r), | |
| ), | |
| ) | |
| reviewer_index = {r: i for i, r in enumerate(reviewers)} | |
| ranked = sorted( | |
| eligible, | |
| key=lambda r: ( | |
| round(scores[r], 6), | |
| last_assigned[r], | |
| reviewer_index[r], | |
| ), | |
| ) |
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This will give us more visibility into the results to make sure it's working as expected and to fine tune the logic if we want.