Executive Summary

Analyzed Period: 2025-09-29 โ†’ 2025-10-13 (15 days, 70 commits)

70
Commits
1
Contributors
187
Files Changed
216.5K
Code Churn
100%
Bus Factor

Current Repository State

Current filesystem analysis showing all files present in the repository today, regardless of Git history.

153
Total Files
1.5 MB
Total Size
29
File Types
10
Directories

File Extensions

Most common file types in the repository

Extension Files % Size
.md 89 58.2% 605.4 KB
.ts 32 20.9% 523.9 KB
.json 4 2.6% 273.7 KB
.js 2 1.3% 1.6 KB
.html 2 1.3% 161.5 KB
.husky\pre-commit 1 0.7% 22 B
.gitignore 1 0.7% 1 B
.husky\_\applypatch-msg 1 0.7% 39 B
.husky\_\commit-msg 1 0.7% 39 B
.husky\_\h 1 0.7% 551 B

File Categories

Files grouped by type and purpose

Category Files % Size
Documentation 81 52.9% 567.1 KB
Other 24 15.7% 176.9 KB
Tests 22 14.4% 193.8 KB
SourceCode 20 13.1% 370.0 KB
Configuration 6 3.9% 275.1 KB

Directory Breakdown

Largest directories by file count

Directory Files %
copilot\session-2025-09-30 40 26.1%
.husky\_ 17 11.1%
test 14 9.2%
copilot\session-2025-10-03 12 7.8%
copilot\session-2025-09-29 10 6.5%
(root) 9 5.9%
copilot\session-2025-10-08 7 4.6%
copilot\session-2025-10-07 5 3.3%

Top Contributors (Author Metrics)

Individual developer metrics calculated from Git commit data. These represent observable activity patterns, not productivity or performance.

AuthorCommitsLines ChangedAvg Commit SizeFiles Touched
Mark Hazleton 70 +132,357 / -84,185 3093.5 492

Team Activity Patterns (Aggregate Metrics)

Repository-wide patterns calculated from all Git commits. These show activity distribution, not team performance or quality.

Commit Distribution

Total Commits 70
Avg per Day 5.0
Active Days 14

Code Volume

Total Churn 216.5K
Files Changed 187

Contributor Patterns

Total Authors 1
Bus Factor 100% Percentage of authors for 50% of commits

๐Ÿ—“๏ธ Contributions Calendar

Activity from 2025-09-29 to 2025-10-13 (9 active days) 70 total commits
Less
More

File Activity Hotspots

Source code files with the most Git activity. High activity may indicate maintenance hotspots but does not imply code quality issues.

FileCommitsLines ChangedAuthorsActivity Score
src/output/html.ts 24 5,562 1 59%
src/core/analyzer.ts 17 3,701 1 48%
src/types/index.ts 9 958 1 26%
src/core/collector.ts 11 798 1 26%
scripts/cli-html-demo.ts 3 776 1 24%
src/cli/commands.ts 9 754 1 25%
src/core/daily-trends.ts 2 726 1 23%
src/index.ts 4 500 1 22%
scripts/test-html-comprehensive.ts 2 372 1 21%
src/output/console.ts 4 342 1 21%

Author Activity Details

Detailed activity patterns for individual contributors. All metrics are derived from Git commit data and represent observable patterns only.

Mark Hazleton

mark@markhazleton.com
Active: 2025-09-29 โ†’ 2025-10-13 (14 days)

Observable Activity Metrics

70
Commits
187
Files
+132,357
Lines Added
-84,185
Lines Deleted
216,542
Code Churn
Avg: 3093.5 lines/commit โ€ข 7.0 files/commit โ€ข 5.00 commits/day

Commit Timing Patterns

โš ๏ธ Note: Timing patterns reflect when commits were made, not actual working hours or availability.

Most Active Day: Tuesday (38.6% of commits)
Most Active Time: Early Morning (6-9am)
Weekend Commits: 4.3%
After-Hours Commits: 32.9%

File Activity Focus

Primary Directories: package.json/ (35%), copilot/session-2025-09-30/ (33%), package-lock.json/ (31%)
Repository Coverage: 100.0% of total files
Source Code Changes: +18,781 / -6,096 (31 commits)

File Type Activity

Documentation
44.4%
Tests
26.2%
SourceCode
12.3%
Configuration
4.3%
Other
3.7%
Top File Extensions
.md 48.7%
.ts 19.3%
.html 11.8%
.json 4.8%
.js 2.1%

Commit Size Distribution

Micro (<20)
14 (20.0%)
Small (20-50)
4 (5.7%)
Medium (51-200)
10 (14.3%)
Large (201-500)
12 (17.1%)
Very Large (>500)
30 (42.9%)

Activity Details

Largest Single Change: 67,843 lines (118f11f) on 10/12/2025
"Clean up repository: Remove 18 temporary test output folders"

Activity Summary: This author contributed 70 commits over 14 active days, changing 187 unique files with an average of 3093.5 lines per commit.

โš ๏ธ Important: Measurement Limitations

What Git Repository Data Can and Cannot Tell Us

โœ… Available from Git Repository

  • Commit metadata (author, timestamp, message)
  • File changes (additions, deletions, modifications)
  • Branch and merge history
  • Authorship and co-authorship information
  • Commit relationships and ancestry

โŒ NOT Available from Git Repository

  • Code review data: No reviewer info, approval status, or review comments
  • Pull/merge request metadata: No PR numbers, descriptions, or review workflows
  • Issue tracking: No bug reports, feature requests, or issue relationships
  • Team structure: No organizational hierarchy, roles, or responsibilities
  • Work hours/timezones: No actual working hours or availability
  • Performance metrics: No build times, test results, or runtime performance
  • Code quality: No actual defect rates, test coverage, or maintainability scores

๐Ÿ“Š Our Approach: Honest, Observable Metrics Only

All metrics in this report are calculated exclusively from Git commit history. We do not guess, estimate, or infer team performance, code quality, or individual productivity from Git data alone.

Author Metrics (Individual)
  • Commit Count: Number of commits authored
  • Lines Changed: Sum of insertions and deletions
  • Commit Size Distribution: Pattern of small vs large commits
  • Active Days: Number of days with at least one commit
  • Files Touched: Number of unique files modified
Team Metrics (Aggregate)
  • Commit Frequency: Total commits per time period
  • Code Churn: Total lines changed across repository
  • Batch Size Distribution: Average and variation in commit sizes
  • Active Contributors: Number of distinct authors in time window
  • File Hotspots: Files with highest number of changes
๐ŸŽฏ Appropriate Usage Guidelines
  • DO: Use to understand activity patterns and contribution distribution
  • DO: Identify files that change frequently (maintenance hotspots)
  • DO: Track repository activity trends over time
  • DON'T: Use for performance reviews or productivity assessments
  • DON'T: Assume commit count equals productivity or value
  • DON'T: Draw conclusions about code quality from Git metrics alone

Calculation Documentation

This section provides detailed explanations of the metrics and calculations used, all based exclusively on Git repository commit data.

๐Ÿ“ Measurement Principles

Objective Data Only

All metrics are calculated from observable Git commit data without interpretation or speculation about team dynamics, productivity, or code quality.

Transparent Limitations

We clearly state what our metrics can and cannot measure, avoiding false claims about team performance or code quality assessment.

No Speculation

We do not infer work-life balance, collaboration effectiveness, or individual performance from Git commit patterns alone.

Repository Activity Index

The Activity Index provides a normalized measure of repository activity based on observable Git patterns.

Activity Index Formula

Activity Index = (Commit Frequency + Author Participation + Change Consistency) รท 3
  • Commit Frequency: Daily commit rate normalized to 0-1 scale
  • Author Participation: Author-to-commit ratio indicating contribution spread
  • Change Consistency: Variation in commit sizes (derived component)
โš ๏ธ Important: This index measures repository activity patterns, not project health, team performance, or code quality. High activity doesn't necessarily indicate good outcomes, and low activity doesn't indicate problems.

Author Activity Metrics

Individual contributor metrics focus on observable activity patterns from Git commit history.

Core Activity Metrics

  • Commit Count: Total number of commits authored in the analysis period
  • Lines Changed: Sum of all line insertions and deletions across all commits
  • Average Commit Size: Mean number of lines changed per commit
  • Files Touched: Number of unique files modified by the author
  • Active Days: Number of distinct days with at least one commit
Lines Changed = Total Insertions + Total Deletions Average Commit Size = Total Lines Changed รท Total Commits

Commit Size Distribution

Classification of commits by the number of lines changed, showing different development patterns:

  • Micro (<20 lines): Small fixes, minor changes
  • Small (20-50 lines): Focused changes, bug fixes
  • Medium (51-200 lines): Feature additions, moderate refactoring
  • Large (201-500 lines): Significant features, major changes
  • Very Large (>500 lines): Major features, large refactors, or merged changes

Note: Commit size alone does not indicate quality, complexity, or effort. Large commits may represent legitimate batch changes, while small commits may address complex issues.

Temporal Patterns

Observable timing patterns in commit history:

  • Commit Timing: Distribution of commits across hours and days (timestamp analysis only)
  • Activity Periods: Identification of high and low activity periods
  • Consistency: Regularity of contributions over time
โš ๏ธ Timing Limitations: Commit timestamps reflect when commits were made, not actual working hours. They can be affected by time zones, commit strategies, and development workflows. Do not use for work-life balance assessment.

Team Activity Patterns

Aggregate Repository Metrics

  • Total Commits: Complete count of commits in the analysis period
  • Code Churn: Total lines inserted and deleted across all commits
  • Active Contributors: Number of unique authors with commits in the period
  • File Activity: Number of unique files modified during the period
  • Bus Factor: Percentage of contributors needed to account for 50% of commits
Bus Factor = (Minimum authors needed for 50% of total commits รท Total authors) ร— 100% Daily Commit Average = Total Commits รท Active Days

File Activity Hotspots

File Activity Analysis

  • File Commit Count: Number of commits that modified each file
  • File Line Changes: Total lines added and removed for each file
  • File Author Count: Number of different authors who modified each file
  • Activity Score: Composite score based on commit frequency and author count

Interpretation: Files with high activity scores are changed frequently and/or by many authors. This may indicate:

  • Core functionality that requires frequent updates
  • Configuration files that change with features
  • Files that need architectural attention
  • Areas of active development
โš ๏ธ Activity โ‰  Problems: High file activity does not necessarily indicate problems, bugs, or poor code quality. Many legitimate factors can cause frequent file changes.

Methodology and Data Sources

Data Source: All metrics are calculated exclusively from Git commit history using standard Git commands (git log, git show, git diff).

Scope: Analysis covers the specified date range and branch(es) with all calculations based on available commit data.

Normalization: Some metrics are normalized to 0-100 scale for consistency and comparison across different repository sizes.

Accuracy: Metric accuracy depends on the completeness and quality of Git commit history. Repositories with missing history or non-standard workflows may show different patterns.

No External Data: We deliberately avoid integrating external data sources (issue trackers, CI/CD systems, code quality tools) to maintain transparency about what Git data alone can and cannot reveal.

Report Metadata

Generated
10/13/2025, 7:33:24 AM
Version
1.0.223
Branch
main
Commit
fea6f7c2
Processing Time
1.53s
Repo Path
C:\GitHub\MarkHazleton\git-spark
CLI Arguments
--format html --output ./docs --days 30