Run2 gh pr analysis

GitHub PR Analysis from Search API Results

Input Format

Each PR item from /search/issues has:

• state: "open" or "closed" (merged PRs are also "closed")
• created_at: ISO 8601 string e.g. "2024-12-05T10:00:00Z"
• pull_request.merged_at: timestamp string or null
• user.login: author username

Classifying PRs

def classify_prs(prs):
    merged, closed, open_prs = [], [], []
    for pr in prs:
        pr_field = pr.get("pull_request") or {}
        merged_at = pr_field.get("merged_at")
        if merged_at:
            merged.append({**pr, "_merged_at": merged_at})
        elif pr["state"] == "closed":
            closed.append(pr)
        else:
            open_prs.append(pr)
    return merged, closed, open_prs

Average Merge Time (days, rounded to 1 decimal)

from datetime import datetime, timezone

def parse_dt(s):
    return datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc)

def avg_merge_days(merged_prs):
    if not merged_prs:
        return 0.0
    diffs = []
    for pr in merged_prs:
        pr_field = pr.get("pull_request") or {}
        merged_at = pr_field.get("merged_at")
        created_at = pr.get("created_at")
        if merged_at and created_at:
            delta = (parse_dt(merged_at) - parse_dt(created_at)).total_seconds() / 86400
            diffs.append(delta)
    return round(sum(diffs) / len(diffs), 1) if diffs else 0.0

Top Contributor

from collections import Counter

def top_contributor(prs):
    authors = Counter(pr["user"]["login"] for pr in prs if pr.get("user"))
    return authors.most_common(1)[0][0] if authors else "unknown"

Key Distinction: "closed" means unmerged+closed

• merged: pull_request.merged_at is not null
• closed: state == "closed" AND pull_request.merged_at is null
• These two are mutually exclusive and exhaustive for closed PRs.