[nlp-analysis] Copilot PR Conversation NLP Analysis - 2026-02-10

[github-actions[bot]]

Executive Summary

Analysis Period: Last 24 hours (merged PRs only)
Repository: github/gh-aw
Total PRs Analyzed: 22
Total Messages: 22 PR bodies analyzed
Average Sentiment: +0.133 (Slightly Positive)

Key Finding: All analyzed Copilot PRs show neutral to positive sentiment, with 59% positive and 41% neutral. Zero negative sentiment detected, indicating high-quality PR descriptions and implementation clarity.

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Sentiment Analysis

Overall Sentiment Distribution

Key Findings:

• Positive PRs: 13 (59%) - Clear problem descriptions and solution explanations
• Neutral PRs: 9 (41%) - Technical, factual implementation details
• Negative PRs: 0 (0%) - No negative language detected
• Average polarity: +0.133 on scale of -1 (very negative) to +1 (very positive)
• Standard deviation: 0.131 - Consistent sentiment across PRs

PRs by Sentiment Category

Observations:

• Strong positive bias in PR descriptions
• No frustrated or negative language patterns
• Neutral PRs are technical and implementation-focused
• Copilot consistently explains changes clearly and constructively

Sentiment Evolution Across PRs

Observations:

• Sentiment remains consistently positive to neutral throughout the period
• Peak positive sentiment: PR [WIP] Update troubleshooting link to existing documentation page #14659 (documentation update)
• No declining sentiment trends - stable quality across time
• Slight variance around neutral baseline indicates factual, technical language

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Topic Analysis

Identified Discussion Topics

Major Topics Detected:

1. Topic 0 - Workflow & Agent Infrastructure (7 PRs, 32%)

   • Keywords: workflow, step, agent, set, job
   • Focus: GitHub Actions workflow structure and agent commands
   • Example PRs: Apply strict matching to slash commands (startsWith + exact equality) #14702, Fix detection job checkout failure from missing contents permission #14698, Move aw_info.json generation before secret validation in compiled workflows #14670

2. Topic 3 - Testing & Repository Management (6 PRs, 27%)

   • Keywords: workflows, test, issue, root, runs
   • Focus: Test infrastructure and repository organization
   • Example PRs: Fix: actions-lock.json created relative to CWD instead of repository root #14727, Allow research workflows to run during release mode #14668, Fix log analyzer path mismatches after artifact download #14660

3. Topic 1 - Issue Templates & Updates (5 PRs, 23%)

   • Keywords: template, noop, update, issue, runs
   • Focus: Issue templates, workflow updates, and noop handling
   • Example PRs: [WIP] Update troubleshooting link to existing documentation page #14659, Add report-as-issue field to safe-outputs.noop #14644, Apply progressive disclosure to no-op runs issue template #14636

4. Topic 2 - Security & MCP Integration (4 PRs, 18%)

   • Keywords: mcp, safe, credentials, git, outputs
   • Focus: Security improvements and MCP server configuration
   • Example PRs: Fix shell injection in generate_git_patch.cjs and push_repo_memory.cjs via shared git_helpers.cjs #14724, Fix API key masking timing vulnerability in MCP setup generation #14701, Add git credentials cleanup and regeneration for agent execution #14700

Topic Word Cloud

Dominant Themes: Workflow infrastructure, issue management, testing, security, and MCP integration are the most prominent discussion areas.

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Keyword Trends

Most Common Keywords and Phrases

Top Recurring Terms:

Technical Focus:

• workflow (0.097) - Primary focus on workflow management
• workflows (0.085) - Plural indicates multiple workflow handling
• mcp (0.079) - MCP server integration prominent
• git (0.078) - Version control operations
• test (0.067) - Testing infrastructure

Action-Oriented:

• issue (0.086) - Issue tracking and resolution
• template (0.083) - Template improvements
• step (0.068) - Workflow step configuration
• runs (0.059) - Workflow execution

Quality & Security:

• security (0.065) - Security improvements prominent
• safe (included in MCP topic) - Safe outputs and inputs

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Conversation Patterns

PR Body Analysis

Content Structure Observed:

• Average PR body length: ~500-2000 words
• All PRs include:
  • Problem description
  • Root cause analysis
  • Changes summary
  • Implementation details
  • Original issue/prompt context

Language Quality:

• Clear, technical language
• Structured markdown formatting
• Code examples and technical details
• No ambiguous or unclear descriptions

Copilot Signature Elements:

• Detailed "Root cause" sections
• Structured "Changes" lists
• "Benefits" sections explaining impact
• Original prompt in collapsible details
• Links to related issues

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Insights and Trends

🔍 Key Observations

1. Universally Positive Sentiment: Zero negative PRs indicates Copilot maintains constructive, solution-focused language even when describing bugs or issues.

2. Topic Diversity: Four distinct topic clusters show Copilot handles diverse work types effectively - from security fixes to documentation updates.

3. Security & Safety Emphasis: "security" and "safe" keywords appear frequently, indicating strong focus on secure coding practices.

4. Clear Problem Articulation: High sentiment scores correlate with well-structured problem descriptions and thorough explanations.

5. Workflow Infrastructure Dominance: 32% of PRs focus on workflow and agent infrastructure, reflecting core product development.

📊 Trend Highlights

• Positive Pattern: Documentation and template updates show highest positive sentiment (PR [WIP] Update troubleshooting link to existing documentation page #14659: +0.406)
• Neutral Pattern: Infrastructure and testing PRs are neutral - focused on technical accuracy over persuasive language
• Emerging Theme: MCP integration appears in 18% of PRs, indicating ongoing feature development
• Quality Indicator: Zero negative sentiment suggests high PR description quality standards

💡 Insights for Prompt Engineering

1. Effective Patterns:

   • Structured sections (Problem → Root Cause → Changes → Benefits)
   • Code examples in explanations
   • Links to related issues/context
   • Clear, technical language

2. Topic Balance:

   • Workflow infrastructure (32%)
   • Testing & repo management (27%)
   • Issue templates & updates (23%)
   • Security & MCP (18%)

3. Language Style:

   • Technical but clear
   • Solution-focused (not problem-focused)
   • Structured and scannable
   • Includes context and rationale

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Sentiment by Topic Cluster

Topic	Focus Area	Avg Sentiment	PR Count
0	Workflow & Agent	+0.12	7
3	Testing & Repository	+0.14	6
1	Templates & Updates	+0.18	5
2	Security & MCP	+0.11	4

Interpretation: Documentation/update PRs have slightly higher sentiment, while infrastructure and security PRs are more neutral (factual).

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PR Highlights

Most Positive PR 😊

PR #14659: [WIP] Update troubleshooting link to existing documentation page
Sentiment: +0.406
Topic: Templates & Updates
Summary: Documentation update with clear improvement description. Positive language reflects helpful, user-focused change.

Largest Topic Cluster 🔧

Topic 0: Workflow & Agent Infrastructure
PRs: 7 (32%)
Summary: Core infrastructure work on workflow management, agent commands, and GitHub Actions integration.

Security Focus 🔒

Topic 2: Security & MCP Integration
PRs: 4 (18%)
Key PRs: #14724 (Shell injection fix), #14701 (MCP credentials), #14700 (Git security)
Summary: Strong emphasis on security improvements and safe credential handling.

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Historical Context

This is the first automated NLP analysis run for Copilot PRs. Historical comparison will be available after multiple runs.

Date	PRs	Avg Sentiment	Top Topic
2026-02-10	22	+0.133	workflow (infrastructure)

Baseline Established: Future analyses will compare against this baseline to identify sentiment trends and topic shifts.

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Recommendations

Based on NLP analysis of 22 Copilot PRs:

🎯 Maintain Current Practices

1. Structured PR Descriptions: Continue using Problem → Root Cause → Changes → Benefits format - correlates with high clarity
2. Technical Precision: Neutral-to-positive sentiment with technical focus is ideal for engineering PRs
3. Context Linking: Including original issue/prompt context enhances understanding

✨ Best Practices Identified

1. Clear Problem Statements: All PRs include explicit problem descriptions
2. Root Cause Analysis: Technical explanations build confidence in solutions
3. Code Examples: Inline code snippets clarify implementation
4. Benefit Statements: Explaining "why" improves reviewability

🔍 Areas to Monitor

1. Topic Balance: 32% workflow focus - ensure diversity across product areas
2. Documentation PRs: Highest sentiment (+0.406) - encourage documentation improvements
3. Security Emphasis: 18% security-focused - maintain security prioritization

💡 Prompt Engineering Insights

For optimal Copilot PR quality:

• Emphasize structured sections in prompts
• Request root cause analysis for bugs
• Include "benefits" or "impact" sections
• Link to related issues for context
• Use technical, precise language (not marketing language)

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Methodology

NLP Techniques Applied

Sentiment Analysis:

• Library: TextBlob
• Polarity range: -1 (negative) to +1 (positive)
• Applied to PR title + body combined
• Categories: Negative (<-0.1), Neutral (-0.1 to +0.1), Positive (>+0.1)

Topic Modeling:

• Algorithm: K-means clustering on TF-IDF vectors
• Number of clusters: 4 (auto-adjusted based on dataset size)
• Features: 50 most important n-grams (1-2 words)
• Cluster labeling: Top 5 terms per cluster

Keyword Extraction:

• Method: TF-IDF (Term Frequency-Inverse Document Frequency)
• N-gram range: 1-2 words
• Top 15 keywords by average TF-IDF score
• Stopwords: English + code-specific terms filtered

Text Preprocessing:

1. Markdown code block removal
2. URL and HTML tag stripping
3. Special character removal
4. Tokenization and lowercasing
5. Stopword removal (English + custom)
6. Lemmatization (WordNet)

Data Sources

• PR Metadata: GitHub GraphQL API (title, body, merged date)
• Time Range: Last 24 hours (2026-02-09 to 2026-02-10)
• Filter: Merged PRs authored by app/copilot-swe-agent
• Sample Size: 22 PRs

Libraries Used

• NLTK: Tokenization, stopwords, lemmatization
• TextBlob: Sentiment analysis
• scikit-learn: TF-IDF vectorization, K-means clustering
• WordCloud: Word cloud visualization
• Pandas/NumPy: Data processing and statistics
• Matplotlib/Seaborn: Chart generation (300 DPI, publication quality)

Limitations

1. Comment Data Unavailable: Analysis limited to PR bodies only (conversation threads not analyzed)
2. Small Sample: 22 PRs - larger samples would improve topic clustering accuracy
3. Single Day: No historical trends available yet
4. Sentiment Tool: TextBlob optimized for general text, not code-specific language
5. English Only: Non-English PRs (if any) not properly analyzed

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Data Artifacts

Stored in Repo Memory (memory/nlp-analysis branch):

• nlp-analysis-2026-02-10.json - Complete analysis results with metrics

Stored in Cache Memory:

• nlp-history.json - Historical analysis data for trend tracking

Generated Visualizations (6 charts):

• Sentiment distribution histogram
• Sentiment categories bar chart
• Sentiment timeline line chart
• Topic frequency bar chart
• Keyword trends horizontal bar chart
• Topic word cloud

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Workflow Details

• Repository: github/gh-aw
• Run ID: §21861014543
• Analysis Date: 2026-02-10
• Analysis Period: Last 24 hours (merged PRs)
• Data Source: Pre-fetched PR metadata + bodies

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References:

• §21861014543 - This workflow run

AI generated by Copilot PR Conversation NLP Analysis

• expires on Feb 17, 2026, 10:39 AM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-02-17T10:39:04.936Z.

Closed by Workflow