[nlp-analysis] Copilot PR Conversation NLP Analysis - 2026-09-24

[github-actions[bot]]

🤖 Copilot PR Conversation NLP Analysis - 2026-09-24

Executive Summary

Analysis Period: Last 7 days (merged PRs only)
Repository: github/gh-aw
Total PRs Analyzed: 77
Total Messages: 77 (PR body text; no review/comment threads were available in the pre-fetched comment data for this run — all pr-comments/pr-*.json files were empty)
Average Sentiment: 0.0602 (positive)

Data Note: The pre-fetched PR comment files (/tmp/gh-aw/agent/pr-comments/pr-*.json) contained no comment/review data for any of the 77 merged PRs analyzed. This analysis is therefore based on PR title and body text only. All 77 PRs are counted as "merged without discussion" for this reason, not due to genuinely quiet conversations.

Sentiment Analysis

Overall Sentiment Distribution

Key Findings:

• Positive messages: 34 (44.2%)
• Neutral messages: 24 (31.2%)
• Negative messages: 19 (24.7%)
• Average polarity: 0.0602 on scale of -1 (very negative) to +1 (very positive)

Sentiment Over Conversation Timeline

Observations:

• Sentiment across the merge window trended mildly positive, consistent with routine feature/fix PRs rather than contentious ones.
• Since only PR-body text was available, this chart reflects sentiment ordered by PR creation time rather than true within-conversation escalation/de-escalation.

Topic Analysis

Identified Discussion Topics

Major Topics Detected:

1. workflow, failure, agent (28 messages, 36.4%): recurring terms — workflow, failure, agent, issue, job, actions, repo, memory
2. network, workflow, run (24 messages, 31.2%): recurring terms — network, workflow, run, aic, sous chef, sous, chef, generated
3. copilot, web, cli (11 messages, 14.3%): recurring terms — copilot, web, cli, workflow, search, web search, engine, copilot cli
4. jira, token, artifact (8 messages, 10.4%): recurring terms — jira, token, artifact, run, usage artifact, usage, audit, comparison

Topic Word Cloud

Keyword Trends

Most Common Keywords and Phrases

Top Recurring Terms:

• Technical: workflow, run, aic, generated, github
• Action-oriented: workflow, run, generated, add, fix
• Feedback: n/a (no review/comment text available this period)

Conversation Patterns

User ↔ Copilot Exchange Analysis

Typical Exchange Pattern:

• Average messages per PR: 1.0 (PR body only — no comment threads captured)
• Average Copilot responses: n/a (comment data unavailable)
• Average user follow-ups: n/a (comment data unavailable)

Engagement Metrics:

• PRs with active discussion (>3 messages): 0 (no comment data captured)
• PRs merged without discussion (as counted from available data): 77
• Average response time: not available (timestamps for comments were not present)

Insights and Trends

🔍 Key Observations

1. Data availability gap: All 77 merged PRs in this window had empty comment/review payloads in the pre-fetched cache, so sentiment and topics reflect PR descriptions rather than back-and-forth discussion — treat conclusions as directional only.

2. Mildly positive baseline: Average polarity of 0.0602 suggests PR descriptions are written in a neutral-to-positive, solution-oriented tone (e.g., "Fix X", "Improve Y", "Preserve Z").

3. Dominant themes: The largest clusters center on workflow/agent infrastructure (failure handling, agent detection, memory) and Copilot CLI/engine configuration, matching the repository's focus on gh-aw tooling.

📊 Trend Highlights

• Positive Pattern: PRs framed around "improve", "preserve", and "fix" language cluster toward higher sentiment scores (e.g., PR Preserve imported engine auth with root engine preferences #62413 preserving engine auth scored 0.600).
• Concerning Pattern: PRs addressing failure/prevention scenarios (e.g., "Prevent Copilot CLI staging", "Allow line inspection in PR code quality reviews") scored most negative (~-0.50), likely reflecting problem-description language rather than actual conflict.
• Emerging Theme: Continued heavy focus on workflow diagnostics, agent-failure handling, and checkout/credential correctness this period.

Sentiment by Message Type

Message Type	Avg Sentiment	Count	Percentage
PR Body	0.0602	77	100%
Comments	n/a	0	0%
Reviews	n/a	0	0%
Review Comments	n/a	0	0%

PR Highlights

Most Positive PR 😊

PR #62413: Preserve imported engine auth with root engine preferences
Sentiment: 0.600
Summary: Framed around preserving/improving existing functionality, using affirming language ("preserve", "root engine preferences").

Most Discussed PR 💬

PR #63048: Improve detection and diagnostics for missing Copilot custom agent
Messages: 1 (comment/review data unavailable this period)
Summary: All PRs registered exactly one analyzable message (the PR body) since comment threads were not present in the pre-fetched data.

Notable Topics PR 🔖

PR #63048: Improve detection and diagnostics for missing Copilot custom agent
Topics: workflow diagnostics, agent failure handling
Summary: Representative of the largest discussion cluster this period (workflow/agent failure & diagnostics).

Historical Context

Date	PRs	Avg Sentiment	Top Topic
2026-09-11	140	0.0245	enclave, github, path, playwright, workflow, runtime
2026-09-15	118	-0.0177	workflow, github, workflows, pull, safe, output
2026-09-22	54	0.105	em, sub, run, domain, network, summary
2026-09-24	77	0.0602	workflow, failure, agent

7-Day Trend: Sentiment decreased by 0.045 vs. previous recorded run (2026-09-22, avg=0.105).

Recommendations

Based on NLP analysis:

1. 🎯 Focus Areas: Continue clear, action-oriented PR descriptions ("Fix", "Improve", "Preserve") — these correlate with more positive sentiment scoring in this dataset.

2. ⚠️ Watch For: The comment/review data pipeline for this workflow returned empty payloads for all 77 PRs this run. Investigate whether the pr-comments pre-fetch step is filtering, rate-limited, or misconfigured, since genuine conversation-level sentiment/topic analysis requires that data.

3. ✨ Best Practices: Maintain concise PR bodies with clear "Changes" sections — the dominant workflow/agent-diagnostics cluster all followed this structure and scored neutral-to-positive.

Methodology

NLP Techniques Applied:

• Sentiment Analysis: TextBlob
• Topic Modeling: TF-IDF + K-means clustering
• Keyword Extraction: N-gram frequency analysis (CountVectorizer)
• Text Preprocessing: Regex cleaning (code blocks, HTML, URLs), lowercase, stopword removal

Data Sources:

• GitHub PR metadata (title, body) — copilot-prs.json
• PR comments and review threads — attempted via pr-comments/pr-*.json (empty for all 77 PRs this run)

Libraries Used:

• NLTK: stopword removal
• scikit-learn: TF-IDF, K-means clustering, CountVectorizer
• TextBlob: sentiment analysis
• WordCloud: visualization
• Pandas/NumPy: data processing
• Matplotlib/Seaborn: charting

Workflow Details

• Repository: github/gh-aw
• Run ID: 35986829470
• Run URL: https://github.com/github/gh-aw/actions/runs/35986829470
• Analysis Date: 2026-09-24

---

This report was automatically generated by the Copilot PR Conversation NLP Analysis workflow.

Warning

Firewall blocked 1 domain

The following domain was blocked by the firewall during workflow execution:

• o205451.ingest.us.sentry.io

To allow these domains, add them to the network.allowed list in your workflow frontmatter:

network:
  allowed:
    - defaults
    - "o205451.ingest.us.sentry.io"

See Network Configuration for more information.

Generated by 🔬 Copilot PR Conversation NLP Analysis · copilot · auto · 61.4 AIC · ⌖ 9.69 AIC · ⊞ 16.1K · ◷

• expires on Sep 25, 2026, 2:31 AM UTC-08:00

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-09-25T10:31:56.717Z.

Closed by Workflow