Case Study: GitSimulator — Production-Grade Git Simulation

Motivation & Pain Points

Git's most powerful commands (rebase, merge, reset, cherry-pick) are also its most dangerous. One wrong move can rewrite history, lose work, or create conflicts that take hours to resolve. GitSimulator addresses the fundamental problem: Git provides no preview of destructive operations before execution.

• Zero Foresight: No way to see exact outcome before running commands like git reset --hard or git rebase
• Surprise Conflicts: Users only discover conflicts mid-operation, after refs have already changed
• Steep Learning Curve: Understanding merge bases, replay ordering, force-push implications, and detached HEAD states is non-trivial
• CI/CD Risk: Automated pipelines cannot validate dangerous Git operations before execution
• Team Onboarding: New developers lack safe environments to experiment with advanced Git features

GitSimulator's Solution: A safe, read-only simulation environment with visual feedback, conflict prediction, safety analysis, and educational explanations—all backed by production-grade infrastructure (135+ tests, CI/CD, plugin architecture).

Architecture Overview

Production-Grade System Design

GitSimulator follows a clean, modular architecture with strict separation of concerns and production-ready infrastructure:

┌────────────────── CLI Layer (Typer) ──────────────────┐
│  Commands │ Options │ Parsing │ Rich Formatting      │
└───────────────────────┬───────────────────────────────┘
                        ▼
┌───────────── Simulation Dispatcher ──────────────────┐
│  • Command routing                                   │
│  • Plugin hook execution (pre/override/post)         │
│  • Result validation                                 │
└───────────────────────┬──────────────────────────────┘
                        ▼
        ┌───────────────┼───────────────┐
        ▼               ▼               ▼
   ┌─────────┐    ┌─────────┐    ┌─────────┐
   │ Rebase  │    │  Merge  │    │  Reset  │
   │Simulator│    │Simulator│    │Simulator│
   └─────────┘    └─────────┘    └─────────┘
        └───────────────┼───────────────┘
                        ▼
┌──────────────── Core Services ───────────────────────┐
│  Repository (Dulwich) │ Diff Analyzer │ Conflict    │
│                       │               │ Detector    │
└───────────────────────┬──────────────────────────────┘
                        ▼
┌────────────── Git Repository (.git/) ────────────────┐
│          Read-only access via Dulwich                │
└──────────────────────────────────────────────────────┘

Core Components

1. Repository Layer (core/repository.py): Pure Python Git access via Dulwich—no git binary required. Features short SHA resolution (7+ chars), relative refs (HEAD~2), topological sorting, and lazy loading for performance.
2. Simulation Engines (simulation/): Each implements BaseSimulator[T] protocol with validate() and simulate() methods. Covers rebase, merge, reset, cherry-pick with step-by-step conflict tracking.
3. Conflict Detection (simulation/conflict_detector.py): Three-level heuristics (CERTAIN 90%+, LIKELY 70%+, POSSIBLE 50%+) analyzing diff hunks, line overlaps, and structural changes.
4. Safety Analysis System: Weighted risk scoring (LOW/MEDIUM/HIGH/CRITICAL) considering rewrite depth, force-push requirements, orphaned commits, and conflict density.
5. Plugin Architecture (plugins/): Hook, Simulator, and Formatter plugins discovered via entry points. Enables custom simulators, output formats, and policy enforcement.
6. Interactive TUI (tui/app.py): Textual-powered terminal UI with real-time command preview, keyboard navigation, and headless mode for scripting.
7. Snapshot System (snapshot.py): Save/restore repository states in separate .git/git-sim-snapshots/ directory for safe experimentation.

Testing Infrastructure (135+ Tests)

• Unit Tests: Isolated component testing (repository, conflict detector, each simulator)
• Integration Tests: End-to-end command flows with real Git repository fixtures
• Property Tests: Invariant checking (graphs are DAGs, simulations idempotent, no repo mutations)
• Fixture-Based: Multiple repo states (linear history, branched, merge commits, octopus merges, detached HEAD)
• Coverage: 95%+ via pytest with HTML reports

CI/CD Pipeline (GitHub Actions)

Automated workflow on every push and pull request:

1. Code Quality: Ruff linting and formatting checks
2. Type Safety: MyPy strict mode type checking
3. Cross-Platform Testing: pytest across Python 3.11/3.12/3.13 on Linux/macOS/Windows
4. Coverage Enforcement: Require 90%+ test coverage
5. Automated Releases: PyPI publishing on version tags with automated changelog generation

Simulation Pipeline

# High-level pseudocode
plan = parse_command(input)
repo = Repository.open(cwd)
base_state = Snapshot.capture(repo)
commit_graph_before = Graph.build(repo)
engine = select_engine(plan.operation)
analysis = engine.prepare(repo, plan)
conflicts = ConflictDetector.predict(repo, analysis)
new_graph = engine.simulate(commit_graph_before, analysis, conflicts)
safety = SafetyAnalyzer.score(plan, analysis, conflicts, commit_graph_before, new_graph)
return SimulationResult(
  operation=plan.operation,
  before=commit_graph_before,
  after=new_graph,
  conflicts=conflicts,
  safety=safety,
  reversible=Snapshot.can_restore(base_state, new_graph)
)

The pipeline never mutates refs. simulate() returns a CommitGraph clone with projected IDs (virtual hashes derived from parent+tree diff signature) for commits that would be rewritten.

Advanced Conflict Detection Engine

GitSimulator’s conflict detection uses sophisticated heuristics to predict merge/rebase conflicts before execution, with three certainty levels:

Detection Levels

• CERTAIN (90%+ accuracy): Same file with overlapping line ranges; both sides modify identical lines; binary file conflicts; delete vs modify scenarios
• LIKELY (70%+ accuracy): Changes within 3 lines of each other; same function/class modifications; high-churn areas; mode changes combined with content edits
• POSSIBLE (50%+ accuracy): Same file modified in different sections; semantic conflicts (import changes, dependency updates)

Detection Algorithm

for each file changed in OURS:
    if file changed in THEIRS:
        parse diff hunks (line ranges + content)
        for each hunk_ours:
            for each hunk_theirs:
                if hunks_overlap(hunk_ours, hunk_theirs):
                    → CERTAIN conflict (line ranges intersect)
                elif hunks_nearby(hunk_ours, hunk_theirs, threshold=3):
                    → LIKELY conflict (within 3 lines)
                else:
                    → POSSIBLE conflict (same file, different sections)
                    
Apply false positive reduction:
  - Normalize whitespace (tabs/spaces, trailing)
  - Collapse contiguous hunks
  - Filter comment-only diffs
  - Detect rename patterns

Edge Cases Handled

• Binary Files: Marked CERTAIN with advisory for manual inspection
• Renames + Modifications: Path changes on both sides detected via similarity heuristics
• Mode Changes: Executable bit/symlink toggles flagged when combined with content edits
• Octopus Merges: Multi-parent ambiguity triggers structural warnings

Safety Scoring Model

Safety levels aggregate weighted factors (w_i) into a normalized 0–100 risk score:

risk = Σ (w_factor * factor_value)
level =
  risk < 25  -> LOW
  risk < 50  -> MEDIUM
  risk < 75  -> HIGH
  else       -> CRITICAL

• History Rewrite Depth: Number of commits with new virtual hashes.
• Force Push Requirement: Any non-fast-forward ref update on a remote-tracked branch.
• Detached Commits Risk: Commits becoming unreachable unless re-tagged.
• Conflict Density: Conflicts / commits replayed ratio.
• Working Tree Discard: (Reset modes) Count + total size of lost tracked changes.

Performance & Optimization

• Lazy Graph Building: Only load commits needed for visualization (not entire history)
• Diff Caching: Memoize expensive diff operations within simulation session
• Short SHA Indexing: Early abort on first unique match (7+ chars)
• Parallel-Safe Design: Pure functional core with no shared mutable state
• Memory Efficiency: Stream commits instead of loading all into memory
• Cross-Platform: Tested on Linux, macOS, Windows across Python 3.11/3.12/3.13

Production Metrics

• Test Coverage: 95%+ across 135+ comprehensive tests
• CI Success Rate: 99%+ (tracked via GitHub Actions badge)
• PyPI Distribution: v1.0.1 published November 2025
• Supported Platforms: Linux, macOS, Windows × Python 3.11/3.12/3.13
• Dependencies: Minimal (Dulwich, Rich, Typer, Textual)
• Package Size: Lightweight CLI with no Git binary dependency

Developer Experience & Modern CLI

Unified Command Interface

Three equivalent commands reduce memorization overhead:

gitsim rebase main
git-sim rebase main        # Dash-style (git alias friendly)
gitsimulator rebase main   # Full package name

# Unified dispatcher for natural syntax:
gitsim sim "rebase main --autosquash"

Rich Terminal Output

• Summary Tables: Source/target branches, merge base, commit counts, conflict predictions
• Safety Panels: Color-coded danger levels (🟢 LOW, 🟡 MEDIUM, 🟠 HIGH, 🔴 CRITICAL)
• ASCII Commit Graphs: Side-by-side before/after visualization with branch topology
• Conflict Tables: Severity, file paths, line ranges in structured format
• Recovery Instructions: Step-by-step undo commands (git reflog, git reset)

Interactive TUI Mode

Textual-powered interface with:

• Real-time command preview as you type
• Keyboard navigation of commit graphs (arrow keys, vim bindings)
• Conflict hotspot highlighting
• Headless mode for CI/scripting: gitsim tui --headless

Educational Mode

gitsim explain <command> provides interactive learning:

• 📖 Plain-English explanations of Git internals
• 🔍 Step-by-step algorithm breakdowns
• ⚠️ Risk assessment and common pitfalls
• 💡 Best practices and safety tips
• 🔄 Alternative approaches (merge vs rebase, reset modes)

Key Struggles & Solutions

• Diff Normalization: Early false positives due to indentation-only changes → added semantic filtering.
• Virtual Hash Representation: Need stable identifiers without computing real SHA-1 → deterministic short hash from parent hash + file path + line change fingerprint.
• Conflict Density Overcount: Multi-line hunk treated as multiple conflicts → collapsed contiguous ranges.
• Safety Calibration: CRITICAL overstated for benign interactive rebases → tuned force-push weight downward.

Real-World Use Cases

1. Pre-Flight Rebase Audit

Before running git rebase main on a 50-commit feature branch:

gitsim rebase main

# Output shows:
# - 50 commits will be rewritten (new SHAs)
# - 3 CERTAIN conflicts in config.py, auth.py
# - HIGH safety level (force-push required)
# - Recovery: git reflog + git reset --hard ORIG_HEAD

2. CI/CD Merge Safety Gate

GitHub Actions workflow:

- name: Validate merge safety
  run: |
    gitsim merge ${{ github.head_ref }} --json > result.json
    SAFETY=$(jq -r '.safety.level' result.json)
    if [[ "$SAFETY" == "CRITICAL" ]]; then
      echo "::error::Merge too risky - manual review required"
      exit 1
    fi

3. Team Onboarding & Education

New developers learn Git internals safely:

gitsim explain rebase     # How rebase works internally
gitsim explain merge      # Three-way merge algorithm
gitsim explain reset      # SOFT vs MIXED vs HARD modes

# Then experiment with snapshots:
gitsim snapshot create before-rebase
gitsim rebase main
# If confused, restore:
gitsim snapshot restore before-rebase

4. Complex Cherry-Pick Planning

Picking 10 commits from feature-a onto hotfix:

gitsim cherry-pick abc123 def456 789xyz ... --onto hotfix

# Shows:
# - Step-by-step conflict prediction for each commit
# - New commit order and SHAs
# - Cumulative conflict areas

5. Dangerous Reset Scenarios

Visualize impact before discarding work:

gitsim reset HEAD~5 --hard

# CRITICAL warning:
# - 5 commits will become orphaned
# - 12 files in working tree will be lost
# - Staged changes discarded (3 files)
# - Recovery possible via reflog for 30 days

Roadmap & Evolution

✅ Completed (v1.0.1)

• Interactive TUI with Textual framework
• Plugin system (Hook, Simulator, Formatter plugins)
• 135+ comprehensive test suite with 95%+ coverage
• Automated CI/CD pipeline (linting, type checking, multi-platform testing)
• PyPI distribution with automated releases
• Educational explain mode for all major commands
• Snapshot/restore system for safe experimentation

🛠️ In Progress

• AST-based conflict refinement for language-specific detection (Python, JavaScript, TypeScript)
• Stash simulation support
• Internationalization (i18n) for explain mode

📋 Planned (v1.1+)

• Batch mode for CI matrix analysis (test multiple branch combinations)
• Custom policy scoring plugins for organization-specific rules
• Integration with Git hosting APIs (GitHub, GitLab, Bitbucket)
• Web-based visualization export (HTML reports)
• Performance profiling mode for large repositories (10K+ commits)

Lessons Learned & Engineering Insights

Heuristics First, Precision Later

Fast approximations unlock value early. We shipped conflict detection with 70%+ accuracy instead of waiting for 95% precision via AST parsing. Users adopted it immediately, and we can layer in deeper analysis later. Key insight: "Good enough" solutions that ship beat perfect solutions that don't.

Explainability Drives Adoption

Users trust safety scores more when rationale is explicit. The explain mode showing why a rebase is HIGH risk (e.g., "3 commits rewritten + force-push required to origin/main") builds confidence. Lesson: Transparency > black-box algorithms.

Testing Infrastructure Pays Dividends

135+ tests caught edge cases during plugin development that would have been production bugs:

• Octopus merges (multiple parents) breaking graph traversal
• Detached HEAD states causing ref resolution failures
• Ambiguous short SHAs (e.g., abc matching both abc1234 and abc5678)
• Windows path handling differences (backslash vs forward slash)

Investment: Writing comprehensive tests takes time upfront but prevents exponential debugging costs later.

Pure Python = Portability Win

Using Dulwich instead of shelling out to git binary eliminated platform-specific bugs:

• No Windows CMD.exe escaping issues
• No Git version compatibility matrix to maintain
• Works in SSH-only environments (no Git installation required)
• Consistent behavior across platforms (no git config differences)

Trade-off: Dulwich has slightly different rename detection than Git, but consistency across platforms outweighs this.

CI/CD Automation Saves Time

Automated PyPI releases on version tags eliminated manual publishing errors:

• Forgot to update version in pyproject.toml → CI enforces tag-version match
• Incomplete changelog → CI generates from commit messages
• Broken builds shipped to PyPI → CI tests before publishing

ROI: 2 days setup time has saved ~4 hours per release (8 releases so far = 32 hours saved).

Read-Only Design Builds Trust

Guaranteeing zero repository mutations (via Dulwich read-only API) was critical for user adoption. Developers are rightfully cautious about tools that touch .git/. Philosophy: Constraints (read-only) can be features (safety guarantee).