Project 9: Git Bisect Automator — Debug Regressions with Binary Search

A tool that wraps git bisect to automatically find the commit that introduced a bug by running a test script, with support for skip detection, performance optimizations, and detailed reporting.

Quick Reference

Attribute	Value
Difficulty	Intermediate
Time Estimate	1 week
Main Programming Language	Python
Alternative Programming Languages	Bash, Go, Rust
Coolness Level	Level 3: Genuinely Clever
Business Potential	2. The “Micro-SaaS / Pro Tool”
Prerequisites	Basic Git, understanding of binary search
Key Topics	Binary Search on DAGs, Git Bisect State, Test Reliability

1. Learning Objectives

By completing this project, you will:

Implement a working version of: A tool that wraps git bisect to automatically find the commit that introduced a bug by running a test script, with support for skip detection, performance optimizations, and detailed reporting..
Explain the core Git workflow tradeoff this project is designed to surface.
Design deterministic checks so results can be verified and reproduced.
Document operational failure modes and safe recovery actions.

2. All Theory Needed (Per-Concept Breakdown)

Binary Search on DAGs

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Binary Search on DAGs. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Binary Search on DAGs in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Binary Search on DAGs is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

Binary Search on DAGs invariant: A condition that must remain true before and after every operation.
Safety boundary: The point where actions become destructive unless guarded.
Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

Capture current state and constraints.
Evaluate whether Binary Search on DAGs preconditions are satisfied.
Execute the minimal safe transition.
Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

Assuming local success implies team-safe behavior.
Treating policy violations as warnings instead of merge blockers.
Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

Which invariant is most likely to break first under concurrency?
What output proves your tool handled an edge case correctly?
Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

The invariant tied to mutable refs or policy-dependent merge eligibility.
A deterministic transcript showing both success and controlled failure behavior.
Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

Change-management tooling for fast-moving teams.
Incident-safe release workflows with traceable rollback paths.
Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

https://git-scm.com/docs
https://dora.dev/capabilities/trunk-based-development/

Key insights Binary Search on DAGs is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Binary Search on DAGs here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

Write one failing scenario and expected detection output.
Define one invariant and one explicit violation test.

Solutions to the homework/exercises

Use a stale branch or invalid metadata case and assert deterministic error reporting.
Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

Git Bisect State

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Git Bisect State. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Git Bisect State in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Git Bisect State is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

Git Bisect State invariant: A condition that must remain true before and after every operation.
Safety boundary: The point where actions become destructive unless guarded.
Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

Capture current state and constraints.
Evaluate whether Git Bisect State preconditions are satisfied.
Execute the minimal safe transition.
Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

Assuming local success implies team-safe behavior.
Treating policy violations as warnings instead of merge blockers.
Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

Which invariant is most likely to break first under concurrency?
What output proves your tool handled an edge case correctly?
Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

The invariant tied to mutable refs or policy-dependent merge eligibility.
A deterministic transcript showing both success and controlled failure behavior.
Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

Change-management tooling for fast-moving teams.
Incident-safe release workflows with traceable rollback paths.
Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

https://git-scm.com/docs
https://dora.dev/capabilities/trunk-based-development/

Key insights Git Bisect State is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Git Bisect State here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

Write one failing scenario and expected detection output.
Define one invariant and one explicit violation test.

Solutions to the homework/exercises

Use a stale branch or invalid metadata case and assert deterministic error reporting.
Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

Test Reliability

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Test Reliability. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Test Reliability in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Test Reliability is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

Test Reliability invariant: A condition that must remain true before and after every operation.
Safety boundary: The point where actions become destructive unless guarded.
Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

Capture current state and constraints.
Evaluate whether Test Reliability preconditions are satisfied.
Execute the minimal safe transition.
Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

Assuming local success implies team-safe behavior.
Treating policy violations as warnings instead of merge blockers.
Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

Which invariant is most likely to break first under concurrency?
What output proves your tool handled an edge case correctly?
Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

The invariant tied to mutable refs or policy-dependent merge eligibility.
A deterministic transcript showing both success and controlled failure behavior.
Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

Change-management tooling for fast-moving teams.
Incident-safe release workflows with traceable rollback paths.
Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

https://git-scm.com/docs
https://dora.dev/capabilities/trunk-based-development/

Key insights Test Reliability is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Test Reliability here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

Write one failing scenario and expected detection output.
Define one invariant and one explicit violation test.

Solutions to the homework/exercises

Use a stale branch or invalid metadata case and assert deterministic error reporting.
Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

3. Project Specification

3.1 What You Will Build

A tool that wraps git bisect to automatically find the commit that introduced a bug by running a test script, with support for skip detection, performance optimizations, and detailed reporting.

3.2 Functional Requirements

Scope control: Deliver a deterministic and testable implementation.
Correctness: Preserve Git invariants and policy constraints.

3.3 Non-Functional Requirements

Performance: Deterministic execution with documented runtime behavior on representative history sizes.
Reliability: Repeated runs on the same input produce identical outputs.
Usability: Clear CLI or report output for both success and failure cases.

3.4 Example Usage / Output

You’ll have a tool that makes bisect easy and informative:

Example Output:

$ auto-bisect --good v1.0.0 --bad HEAD --test "npm test"

=== Auto Bisect ===
Good: v1.0.0 (abc123)
Bad:  HEAD (xyz789)

Calculating search space...
  Commits between good and bad: 127
  Expected bisect steps: ~7 (log₂(127) = 6.99)

Starting automated bisect...

Step 1/7: Testing commit def456 "Add user profile feature"
  Running: npm test
  Result: GOOD (tests pass)
  Search space: 127 → 63 commits remaining

Step 2/7: Testing commit ghi789 "Refactor authentication"
  Running: npm test
  Result: BAD (tests fail)
  Search space: 63 → 31 commits remaining

Step 3/7: Testing commit jkl012 "Update dependencies"
  Running: npm test
  Result: SKIP (build failed, can't test)
  Skipping this commit, trying adjacent...

  Testing commit jkl011 "Fix linting errors"
  Result: GOOD (tests pass)
  Search space: 31 → 15 commits remaining

... (steps 4-7)

Step 7/7: Testing commit mno345 "Fix login redirect"
  Running: npm test
  Result: BAD (tests fail)

=== BISECT COMPLETE ===

First bad commit: mno345
Author: Alice <alice@example.com>
Date:   2024-01-12 14:32:00

    Fix login redirect

    Changed the redirect URL after successful login
    to use relative paths instead of absolute.

Changed files:
  src/auth/login.ts (+5 -3)
  src/routes/index.ts (+2 -1)

This commit likely introduced the bug!

Suggestion: Check the changes to src/auth/login.ts lines 45-52

$ auto-bisect --log
Previous bisect sessions:
  2024-01-15: Found mno345 (7 steps, 2m 30s)
  2024-01-10: Found abc123 (5 steps, 1m 45s)

3.5 Data Formats / Schemas / Protocols

Describe input repository assumptions, output report shape, and any policy/config schema consumed by the tool.

3.6 Edge Cases

Empty repository or shallow clone state.
Detached HEAD or rewritten history during execution.
Invalid metadata/policy configuration.

3.7 Real World Outcome

You’ll have a tool that makes bisect easy and informative:

Example Output:

$ auto-bisect --good v1.0.0 --bad HEAD --test "npm test"

=== Auto Bisect ===
Good: v1.0.0 (abc123)
Bad:  HEAD (xyz789)

Calculating search space...
  Commits between good and bad: 127
  Expected bisect steps: ~7 (log₂(127) = 6.99)

Starting automated bisect...

Step 1/7: Testing commit def456 "Add user profile feature"
  Running: npm test
  Result: GOOD (tests pass)
  Search space: 127 → 63 commits remaining

Step 2/7: Testing commit ghi789 "Refactor authentication"
  Running: npm test
  Result: BAD (tests fail)
  Search space: 63 → 31 commits remaining

Step 3/7: Testing commit jkl012 "Update dependencies"
  Running: npm test
  Result: SKIP (build failed, can't test)
  Skipping this commit, trying adjacent...

  Testing commit jkl011 "Fix linting errors"
  Result: GOOD (tests pass)
  Search space: 31 → 15 commits remaining

... (steps 4-7)

Step 7/7: Testing commit mno345 "Fix login redirect"
  Running: npm test
  Result: BAD (tests fail)

=== BISECT COMPLETE ===

First bad commit: mno345
Author: Alice <alice@example.com>
Date:   2024-01-12 14:32:00

    Fix login redirect

    Changed the redirect URL after successful login
    to use relative paths instead of absolute.

Changed files:
  src/auth/login.ts (+5 -3)
  src/routes/index.ts (+2 -1)

This commit likely introduced the bug!

Suggestion: Check the changes to src/auth/login.ts lines 45-52

$ auto-bisect --log
Previous bisect sessions:
  2024-01-15: Found mno345 (7 steps, 2m 30s)
  2024-01-10: Found abc123 (5 steps, 1m 45s)

4. Solution Architecture

4.1 High-Level Design

Inputs -> Validation -> Core Engine -> Output Formatter -> Verification Report

4.2 Key Components

Component	Responsibility	Key Decisions
Input loader	Discover commits/refs/config inputs	Deterministic ordering and clear failure messages
Core engine	Compute project-specific logic	Separate read-only simulation from mutating actions
Reporter	Produce user-facing output and evidence	Include machine-readable and human-readable forms

4.4 Data Structures (No Full Code)

ProjectState { refs, commits, policy, findings, metrics }
Result { status, evidence, warnings, next_actions }

4.4 Algorithm Overview

Collect state from repository and configuration.
Evaluate invariants and policy preconditions.
Execute core transformation or analysis logic.
Verify postconditions and emit deterministic report.

Complexity Analysis:

Time: O(history + affected scope)
Space: O(active graph window + report size)

5. Implementation Guide

5.1 Development Environment Setup

Use the environment defined in the main guide. Pin tool versions and fixture data to keep outputs reproducible.

5.2 Project Structure

project-root/
├── fixtures/
├── src/
├── tests/
├── docs/
└── README.md

5.3 The Core Question You’re Answering

“How does binary search apply to debugging, and how does Git leverage the commit graph for bisect?”

Before you write any code, sit with this question. Bisect works because Git history is ordered (parent relationships). Given a known-good and known-bad commit, you can binary search through the DAG to find where things went wrong.

5.4 Concepts You Must Understand First

Stop and research these before coding:

Binary Search on DAGs
- How does bisect work when history isn’t linear?
- How does Git choose the midpoint in a merge-heavy history?
- What’s the worst-case complexity?
- Book Reference: “Pro Git” Ch. 7.10 — Chacon
Git Bisect State
- Where does Git store bisect state?
- What are the bisect commands (start, good, bad, skip, reset)?
- How do you automate bisect with git bisect run?
- Book Reference: “Pro Git” Ch. 7.10 — Chacon
Test Reliability
- What makes a test suitable for bisecting?
- How do you handle commits that can’t be tested (build failures)?
- How do you detect and handle flaky tests?
- Book Reference: “Continuous Delivery” Ch. 8 — Humble & Farley

5.5 Questions to Guide Your Design

Before implementing, think through these:

Test Script Interface
- What exit codes should the test script use (0=good, 1-124=bad, 125=skip)?
- How do you handle timeouts?
- How do you capture and display test output?
Bisect Optimization
- How can you speed up bisect (parallel builds, caching)?
- How do you minimize checkout operations?
- Can you pre-compute which commits are skippable?
Reporting
- What information is most useful when bisect completes?
- How do you present the journey (steps taken)?
- How do you suggest next debugging steps?

5.6 Thinking Exercise

Walk Through Bisect Manually

Simulate bisect on paper:

Commit history: A ← B ← C ← D ← E ← F ← G ← H
                good              BAD           bad

Start: good=A, bad=H (8 commits)

Questions while walking through:

Step 1: Which commit does Git test first (midpoint)?
If midpoint is BAD, what’s the new search range?
If midpoint is GOOD, what’s the new search range?
How many steps maximum to find the first bad commit?
What if commit D can’t be built (skip)?

5.7 The Interview Questions They’ll Ask

Prepare to answer these:

“Explain how git bisect works internally.”
“What’s the time complexity of git bisect?”
“How would you handle a situation where bisect identifies a merge commit as bad?”
“What makes a good test script for automated bisect?”
“How would you bisect a performance regression (not a pass/fail test)?”

5.8 Hints in Layers

Hint 1: Starting Point Use git bisect run ./test-script.sh. Exit code 0 = good, 1-124 = bad, 125 = skip, 126-127 = abort.

Hint 2: Parsing Output Capture bisect output to track progress. Look for “Bisecting:” lines to know which commit is being tested.

Hint 3: Enhanced Reporting After bisect completes, use git show --stat <bad-commit> to show what files changed.

Hint 4: Flaky Detection Run the test multiple times at a commit. If results are inconsistent, mark as flaky and skip.

5.9 Books That Will Help

Topic	Book	Chapter
Git bisect	“Pro Git” by Chacon	Ch. 7.10
Binary search	“Algorithms” by Sedgewick	Ch. 1
Test reliability	“Continuous Delivery” by Humble & Farley	Ch. 8

5.10 Implementation Phases

Phase 1: Foundation (1-2 sessions)

Define fixtures, expected outputs, and invariant checks.
Build read-only analysis path.

Phase 2: Core Functionality (2-4 sessions)

Implement project-specific core logic and deterministic reporting.
Add policy and edge-case handling.

Phase 3: Polish and Edge Cases (1-2 sessions)

Add failure demos, performance notes, and usability improvements.
Finalize docs and validation transcripts.

5.11 Key Implementation Decisions

Decision	Options	Recommendation	Rationale
Execution mode	direct write vs dry-run+write	dry-run+write	Safer and easier to debug
Output contract	free text vs structured+text	structured+text	Better automation and readability
Enforcement location	local only vs local+CI	local+CI	Prevents bypass in shared branches

6. Testing Strategy

6.1 Test Categories

Unit tests for parsing and policy logic.
Integration tests on fixture repositories.
Edge-case tests for stale refs, malformed metadata, and large histories.

6.2 Critical Test Cases

Deterministic golden-path scenario.
Policy violation hard-fail scenario.
Recovery path after partial or conflicting state.

6.3 Test Data

Use fixed repository fixtures with known commit graphs and expected outputs stored under version control.

7. Common Pitfalls & Debugging

Problem 1: “Output looks correct but history or metadata is inconsistent”

Why: Validation happens after mutation, not before.
Fix: Add a preflight invariant check and a post-write verification step.
Quick test: Run the same command twice on the same fixture and verify identical results.

Problem 2: “Tool works on small repo but times out on larger history”

Why: Full traversal is performed where selective traversal is possible.
Fix: Cache intermediate graph lookups and scope analysis to affected commits/paths.
Quick test: Compare runtime on small and large fixtures with a clear budget target.

Problem 3: “Policy check can be bypassed by local-only behavior”

Why: Enforcement is advisory, not server-authoritative.
Fix: Mirror critical checks in CI and protected branch rules.
Quick test: Attempt merge with failing policy in CI and confirm hard block.

8. Extensions & Challenges

8.1 Beginner Extensions

Add richer error messages with remediation hints.
Add fixture generation helpers for repeatable demos.

8.2 Intermediate Extensions

Add performance instrumentation and budget assertions.
Add policy configuration profiles by repository type.

8.3 Advanced Extensions

Add distributed execution support for large repositories.
Add signed evidence exports for compliance workflows.

9. Real-World Connections

9.1 Industry Applications

Internal developer portals.
Enterprise repository governance systems.
Release safety and incident diagnostics tooling.

Git core: https://git-scm.com/
GitHub CLI: https://github.com/cli/cli
pre-commit framework: https://pre-commit.com/

9.3 Interview Relevance

This project prepares you for architecture and debugging interviews that focus on merge policy, CI gates, and workflow reliability tradeoffs.

10. Resources

10.1 Essential Reading

Pro Git (Internals and Workflows chapters)
Software Engineering at Google (Version control and build chapters)
Accelerate (delivery performance practices)

10.2 Video Resources

Git internals talks from Git Merge conference archives.
DORA and delivery metrics conference sessions.

10.3 Tools and Documentation

https://git-scm.com/docs
https://docs.github.com/
https://dora.dev/

11. Self-Assessment Checklist

11.1 Understanding

I can explain the primary invariant this project enforces.
I can explain one failure mode and one safe recovery path.

11.2 Implementation

Functional requirements are met on deterministic fixtures.
Critical edge cases are tested and documented.

11.3 Growth

I can describe tradeoffs in an interview setting.
I documented what I would change in a production version.

12. Submission / Completion Criteria

Minimum Viable Completion:

Deterministic golden-path output exists.
One failure scenario is handled with clear output.
Core workflow objective is demonstrably met.

Full Completion:

Minimum criteria plus policy validation, structured reporting, and edge-case coverage.

Excellence:

Full completion plus measurable performance budget and production-hardening notes.

Project 9: Git Bisect Automator — Debug Regressions with Binary Search

Quick Reference

1. Learning Objectives

2. All Theory Needed (Per-Concept Breakdown)

Binary Search on DAGs

Git Bisect State

Test Reliability

3. Project Specification

3.1 What You Will Build

3.2 Functional Requirements

3.3 Non-Functional Requirements

3.4 Example Usage / Output

3.5 Data Formats / Schemas / Protocols

3.6 Edge Cases

3.7 Real World Outcome

4. Solution Architecture

4.1 High-Level Design

4.2 Key Components

4.4 Data Structures (No Full Code)

4.4 Algorithm Overview

5. Implementation Guide

5.1 Development Environment Setup

5.2 Project Structure

5.3 The Core Question You’re Answering

5.4 Concepts You Must Understand First

5.5 Questions to Guide Your Design

5.6 Thinking Exercise

Walk Through Bisect Manually

5.7 The Interview Questions They’ll Ask

5.8 Hints in Layers

5.9 Books That Will Help

5.10 Implementation Phases

Phase 1: Foundation (1-2 sessions)

Phase 2: Core Functionality (2-4 sessions)

Phase 3: Polish and Edge Cases (1-2 sessions)

5.11 Key Implementation Decisions

6. Testing Strategy

6.1 Test Categories

6.2 Critical Test Cases

6.3 Test Data

7. Common Pitfalls & Debugging

8. Extensions & Challenges

8.1 Beginner Extensions

8.2 Intermediate Extensions

8.3 Advanced Extensions

9. Real-World Connections

9.1 Industry Applications

9.2 Related Open Source Projects

9.3 Interview Relevance

10. Resources

10.1 Essential Reading

10.2 Video Resources

10.3 Tools and Documentation

10.4 Related Projects in This Series

11. Self-Assessment Checklist

11.1 Understanding

11.2 Implementation

11.3 Growth

12. Submission / Completion Criteria