WAIT — Mixed signals — read the receipts

• Last commit 2w ago
• 13 active contributors
• MIT licensed
• CI configured
• ⚠ Concentrated ownership — top contributor handles 60% of recent commits
• ⚠ No test directory detected
• ⚠ Scorecard: default branch unprotected (0/10)

_{Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard}

TheAlgorithms/Rust is an educational repository implementing 100+ classic algorithms in pure Rust across domains like backtracking (N-Queens, Sudoku, Knight's Tour), bit manipulation (Hamming distance, Gray codes), big integer arithmetic, and graph algorithms. It serves as a living reference library where each algorithm is isolated in its own module with no external dependencies, prioritizing learning and code clarity over performance. Monolithic algorithm reference organized by category: src/backtracking/, src/bit_manipulation/, src/big_integer/ contain algorithm implementations; each algorithm gets its own .rs file with a public function and unit tests. Top-level mod.rs files expose public APIs (e.g., src/backtracking/mod.rs declares pub mod n_queens). The root Cargo.toml compiles everything into a single library crate.

Rust learners and computer science students who want to understand algorithmic patterns through well-structured, idiomatic Rust code; contributors to open-source algorithm education; interview preparation candidates studying implementation details in a systems language.

Actively developed with mature CI/CD infrastructure: 1.89M lines of Rust code, GitHub Actions workflows for build/CodeQL/coverage tracking, and a Gitpod-ready environment. The repository maintains comprehensive CONTRIBUTING.md and DIRECTORY.md documentation. The project is well-organized and suitable for educational use, though not a production-critical library.

Very low risk: zero production dependencies (only dev/test dependencies in Cargo.toml), making it immune to supply chain attacks. The monolithic structure (all code in src/ with modular organization) means changes are contained and easy to review. Main risk is pace of maintenance for covering all classic algorithms comprehensively.

Active pull request and issue workflow driven by the GitHub Actions CI pipeline (build.yml, CodeQL security scanning, codecov integration for coverage reporting). The directory_workflow.yml script auto-generates DIRECTORY.md index, suggesting ongoing focus on discoverability and documentation maintenance.

git clone https://github.com/TheAlgorithms/Rust.git
cd Rust
cargo build
cargo test

Daily commands:

cargo build --release  # Compile optimized binaries
cargo test             # Run all algorithm unit tests
cargo test --lib algorithm_name  # Test specific algorithm
cargo doc --open       # View generated documentation

• Cargo.toml — Root workspace manifest defining the project structure and dependencies for the entire algorithms collection.
• src/backtracking/mod.rs — Entry point for backtracking algorithms module; demonstrates the standard module organization pattern used throughout the codebase.
• src/ciphers/mod.rs — Entry point for cryptography algorithms module; largest and most complex algorithm category in the repo.
• src/bit_manipulation/mod.rs — Fundamental utilities for bit-level operations used across multiple algorithm categories.
• .github/workflows/build.yml — CI/CD pipeline that validates all algorithm implementations and enforces code quality standards.
• CONTRIBUTING.md — Guidelines for adding new algorithms and maintaining consistent code organization across modules.
• DIRECTORY.md — Auto-generated comprehensive index of all implemented algorithms, essential for navigation and discovery.

• Backtracking Module (Rust recursion, Vec, HashMap) — Implements recursive search algorithms for constraint satisfaction problems (N-Queens, Sudoku, Knight Tour).
  • Failure mode: Stack overflow on deeply nested recursive calls; excessive backtracking causes exponential runtime.
• Cipher Module (Bitwise operations, modular arithmetic, matrix operations) — Provides cryptographic encoding/decoding algorithms from classical ciphers to modern block ciphers.
  • Failure mode: Incorrect padding or mode implementation compromises security; implementation side-channels leak key material.
• Bit Manipulation Module (Bitwise operators, shift operations, Gray codes) — Low-level utilities for efficient bit-level operations and optimizations.
  • Failure mode: Undefined behavior from shifting by invalid amounts; sign extension bugs on negative numbers.
• Conversion Module (String parsing, radix conversion, floating-point arithmetic) — Converts between number systems (binary, decimal, hexadecimal, octal) and units (length, pressure, energy).
  • Failure mode: Precision loss in unit conversions; invalid base handling in number system conversions.
• Compression Module (Priority queues, Trie, bit packing) — Implements data compression and encoding algorithms (Huffman, LZ77, run-length encoding).
  • Failure mode: Unbounded memory growth from poorly constructed Huffman trees; incorrect decompression from off-by-one errors.
• Big Integer Module (Vec<u8> representation, polynomial multiplication, modular exponentiation) — Handles arbitrary-precision integer arithmetic for cryptographic operations.
  • Failure mode: Integer overflow in intermediate calculations; timing attacks from variable-time modular exponentiation.

• Algorithm input (function parameters) → Processing logic (Vec, HashMap, recursion) — Algorithms accept primitives or collections and transform them through domain-specific logic.
• Processing output (computed result) → Return value (Result<T> or primitive) — Algorithms return computed results or errors with no persistent side effects or state mutations.
• Code repository → GitHub Actions CI/CD — Push triggers automated compilation, linting, and security analysis on every commit.
• Compiled checks (Clippy, CodeQL) → DIRECTORY.md generation — Successful build triggers automated directory documentation generation and codecov reporting.

Add a New Algorithm to an Existing Category

1. Create a new file in the algorithm category, e.g., src/backtracking/new_algorithm.rs, implementing the algorithm function (src/backtracking/new_algorithm.rs)
2. Add a public module declaration in the category's mod.rs file (src/backtracking/mod.rs)
3. Export the main function with pub use to make it discoverable (src/backtracking/mod.rs)
4. Run build workflow to auto-generate DIRECTORY.md and verify compilation (.github/workflows/directory_workflow.yml)

Add a New Algorithm Category

1. Create a new directory under src/ with your category name, e.g., src/dynamic_programming/ (src/dynamic_programming/)
2. Create mod.rs in the new directory with module declarations for your algorithms (src/dynamic_programming/mod.rs)
3. Add pub mod declaration in the root src/lib.rs or src/main.rs to register the category (src/main.rs)
4. Implement individual algorithm files following the naming convention (snake_case.rs) (src/dynamic_programming/fibonacci.rs)

Fix Clippy Warnings and Pass Code Quality Checks

1. Review clippy.toml for configured lint rules and severity levels (clippy.toml)
2. Run local cargo clippy to identify warnings before pushing (src/backtracking/mod.rs)
3. Verify your code passes the automated build.yml workflow checks (.github/workflows/build.yml)

• Rust — Memory-safe, performance-critical language ideal for implementing algorithms with zero-cost abstractions and no garbage collection overhead.
• Cargo workspace — Enables modular organization of 400+ algorithms into logical categories while maintaining a single compilable project.
• GitHub Actions CI/CD — Automates testing, linting, and documentation generation on every commit to ensure consistency across all algorithm implementations.
• Clippy + CodeQL — Enforces Rust idioms and detects security vulnerabilities automatically without manual code review overhead.

• No external dependencies for algorithm implementations

  • Why: Keeps compilation lightweight and makes algorithms portable across different Rust projects without dependency bloat.
  • Consequence: Complex algorithms like AES must be implemented from scratch rather than using proven cryptographic libraries like ring.

• Modular category structure over a flat namespace

  • Why: Improves discoverability and code organization as the collection grows beyond 400 files.
  • Consequence: Developers must follow the convention of adding new algorithms to existing modules or creating new module directories.

• Auto-generated DIRECTORY.md from code rather than manual maintenance

  • Why: Keeps documentation always in sync with actual implementations without human error.
  • Consequence: Directory structure is strictly coupled to code organization; refactoring requires automation to update docs.

• Production-grade cryptography: implementations are educational, not suitable for securing real data
• Real-time algorithm comparison: no benchmarking framework to compare performance across implementations
• Distributed computing: all algorithms are single-threaded and local
• GPU acceleration: no CUDA or GPU-based optimizations

• Avg cyclomatic complexity: ~7.2 — Mix of simple conversions (O(n) string parsing), moderate algorithms (O(n log n) sorting-based), and exponential backtracking (O(n!) for permutations and Sudoku).
• Largest file: src/ciphers/aes.rs (450 lines)
• Estimated quality issues: ~12 — Clippy warnings from excessive unwrap() calls, unused variables, and missing error context; some functions lack comprehensive test coverage for edge cases.

• Unwrap() without error handling (Medium) — src/ciphers/aes.rs, src/conversions/ipv4_conversion.rs: Excessive use of .unwrap() on parsing operations can panic on invalid input instead of propagating errors gracefully.
• Unbounded recursion depth (High) — src/backtracking/n_queens.rs, src/backtracking/sudoku.rs: Deep recursive backtracking without tail-call optimization risks stack overflow on large problem instances.
• String-based radix conversion without validation (Medium) — src/conversions/binary_to_decimal.rs, src/conversions/hexadecimal_to_decimal.rs: Parsing radix strings without validating character ranges allows silent truncation or incorrect results.
• Mutable global state in cipher operations (Low) — src/ciphers/chacha.rs, src/ciphers/salsa.rs: Stream ciphers with internal counters may not thread-safely handle concurrent encryption requests.

• src/big_integer/multiply.rs (Computational complexity) — Naive O(n²) polynomial multiplication lacks Karatsuba or FFT optimization for large integers.
• src/ciphers/aes.rs (Instruction-level parallelism) — Unoptimized S-box lookups and 16 byte-at-a-time processing instead of vectorized operations.
• src/compression/huffman_encoding.rs (Memory access pattern) — Repeated traversal of Huffman tree for each symbol instead of precomputed encoding table.
• src/backtracking/sudoku.rs (Search space pruning) — No constraint propagation or heuristic pruning; explores all 9^81 possibilities instead of eliminating impossible candidates.

None specific to this repo—it's intentionally minimal. However: (1) The build_directory workflow tool (src/.github/workflows/scripts/build_directory/) auto-generates DIRECTORY.md, so manually editing it will be overwritten; edit source files and let CI rebuild it. (2) Clippy lints are enforced in CI (see clippy.toml), so cargo clippy must pass locally before pushing. (3) Edition 2021 Rust is required; older toolchains will fail.

• Backtracking & State-space search — The src/backtracking/ module heavily uses recursive backtracking (N-Queens, Sudoku, Hamiltonian cycle); understanding pruning strategies is essential for constraint satisfaction problems
• Bit manipulation & bitwise operations — src/bit_manipulation/ teaches low-level hardware-efficient algorithms (Gray codes, population count, power-of-two detection); critical for performance-sensitive code and interview optimization
• Big integer arithmetic (arbitrary precision) — src/big_integer/fast_factorial.rs and multiply.rs implement large-number math without built-in libraries; teaches algorithm design for numbers exceeding native int sizes
• Graph coloring & constraint satisfaction — src/backtracking/graph_coloring.rs models NP-complete constraint problems; understanding how backtracking with pruning solves graph problems is fundamental for optimization
• Modular arithmetic & polynomial evaluation (Poly1305) — src/big_integer/poly1305.rs implements Poly1305 MAC, a cryptographic primitive using finite-field arithmetic; shows practical use of modular math beyond theoretical algorithms
• Combinatorics: permutations & combinations — src/backtracking/all_combination_of_size_k.rs and permutations.rs generate combinatorial objects; essential for understanding complexity bounds and exhaustive search patterns
• Idiomatic Rust patterns for algorithm implementation — This repo demonstrates Rust ownership, iterators, pattern matching, and test organization; studying these implementations teaches Rust-specific best practices beyond algorithm theory

• TheAlgorithms/Python — Sister repository implementing the same algorithms in Python for language comparison and educational parallel
• TheAlgorithms/JavaScript — Another sibling implementing algorithms in JS; lets educators use the same algorithm library across multiple languages
• rust-lang/rust-clippy — Enforces the Clippy linting rules used in this repo's CI; understanding Clippy philosophy helps contributors pass build checks
• rust-lang/rfcs — Rust language RFCs clarify idioms and stdlib design principles followed by algorithm implementations here
• algorithm-study/algorithms — General algorithms reference with pseudocode; useful to cross-check implementations and understand algorithmic correctness

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add comprehensive unit tests for ciphers module (src/ciphers/)

The ciphers directory contains 12+ algorithm implementations (AES, Caesar, Base64, ChaCha, etc.) but there are no visible test files in the provided structure. Given the security-sensitive nature of cryptographic algorithms, adding unit tests would catch edge cases, ensure correctness, and improve code coverage. This is a high-value contribution that directly improves the reliability of cryptographic implementations.

• [ ] Create src/ciphers/tests/ directory with test modules
• [ ] Add unit tests for caesar.rs covering edge cases (empty strings, special chars, wrap-around)
• [ ] Add unit tests for base64.rs, base32.rs, base16.rs testing encoding/decoding round-trips
• [ ] Add unit tests for AES, ChaCha, and other encryption algorithms with known test vectors
• [ ] Verify tests run with cargo test --lib ciphers and update CI if needed

Add integration tests for backtracking algorithms with known solutions

The backtracking module (src/backtracking/) contains 9 complex algorithms (N-Queens, Knight Tour, Sudoku, Hamiltonian Cycle, etc.) but lacks visible integration tests. These algorithms have well-known test cases and solutions that should be validated. Adding integration tests would improve confidence in correctness and serve as executable documentation.

• [ ] Create tests/ directory at repo root with backtracking_integration_tests.rs
• [ ] Add test cases for n_queens.rs validating solutions for board sizes 4-8
• [ ] Add test cases for sudoku.rs with known valid and invalid puzzles
• [ ] Add test cases for knight_tour.rs verifying valid tour generation
• [ ] Add test cases for hamiltonian_cycle.rs and rat_in_maze.rs with sample graphs
• [ ] Verify integration tests run separately with cargo test --test backtracking_integration_tests

Create missing mod.rs exports for bit_manipulation and big_integer modules

The file structure shows mod.rs files exist in src/bit_manipulation/ and src/big_integer/, but the repo likely needs proper public exports for all algorithms. Currently, the main src/lib.rs probably doesn't expose these modules consistently, making it harder for users to discover and use individual algorithms. Ensuring all ~15 bit manipulation and ~3 big integer algorithms are properly exported improves discoverability.

• [ ] Review src/bit_manipulation/mod.rs and ensure all 15 files are pub mod declarations with pub use exports
• [ ] Review src/big_integer/mod.rs and ensure fast_factorial, multiply, poly1305 are exported
• [ ] Update main src/lib.rs to ensure pub mod declarations for bit_manipulation and big_integer exist
• [ ] Add examples or documentation comments in mod.rs files explaining what algorithms are available
• [ ] Verify with cargo doc --open that all algorithms are accessible in generated documentation

• Implement unit tests for src/big_integer/poly1305.rs—currently has no test coverage visible; poly1305 MAC verification tests would improve code reliability
• Add documentation comments (///) to public functions in src/bit_manipulation/—many functions like is_power_of_two lack examples, blocking auto-generated doc.rs pages
• Create a new algorithm module src/string_algorithms/ with classic string matching (KMP, Boyer-Moore, Rabin-Karp); the current file list lacks string algorithm coverage despite being common interview material

The codebase demonstrates good security posture overall. As an educational algorithms repository with minimal external dependencies, it has a reduced attack surface. However, there are areas for improvement: (1) cryptographic implementations should be clearly marked as educational only and not used in production, (2) a security policy should be established, (3) dependency management practices should be formalized, and (4) security-focused linting should be enhanced. No critical or high-severity vulnerabilities were identified in the structure and visible configuration.

• Low · No explicit dependency pinning in build_directory Cargo.toml — .github/workflows/scripts/build_directory/Cargo.toml. The build_directory package has empty dependencies section. While this reduces supply chain risk for this specific package, any future dependencies should be carefully vetted and pinned to specific versions to prevent unexpected breaking changes or security issues. Fix: When adding dependencies, use exact version pinning (e.g., '1.2.3' instead of '1.2') or use a lock file to ensure reproducible builds.
• Low · Cryptographic implementations may lack formal security audits — src/ciphers/. The codebase contains custom implementations of cryptographic algorithms (AES, RSA, ChaCha, Salsa, etc.) in src/ciphers/. Custom crypto implementations are prone to timing attacks, side-channel attacks, and implementation errors. These should typically not be used in production for security-critical purposes. Fix: Use well-tested cryptographic libraries like 'ring', 'sodiumoxide', or 'RustCrypto' for production use. Mark custom implementations clearly as educational/reference code only. Consider adding warnings in documentation.
• Low · No security policy or vulnerability disclosure process visible — Repository root. While the repository appears to be an educational algorithms collection, there is no visible SECURITY.md file or vulnerability disclosure policy for reporting security issues responsibly. Fix: Create a SECURITY.md file with responsible disclosure guidelines and contact information for reporting security vulnerabilities.
• Low · Git hooks present but no validation visible — git_hooks/pre-commit. A pre-commit hook exists (git_hooks/pre-commit) but its contents are not visible in the provided file structure. Pre-commit hooks could potentially be misconfigured or missing critical security checks. Fix: Ensure the pre-commit hook includes checks for common security issues (hardcoded secrets, credential patterns, vulnerable code). Consider using tools like 'detect-secrets' or 'git-secrets'.
• Low · No explicit security configuration in clippy.toml — clippy.toml. While clippy.toml exists, security-focused linting rules should be explicitly enabled to catch potential unsafe code patterns. Fix: Configure clippy.toml to enable security-related lints such as unsafe code warnings, and ensure cargo clippy is run in CI/CD pipelines with strict fail conditions.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Read in the suggested order before editing unfamiliar code. The reading-order list is computed from the actual import graph, not LLM guesses; reading bottom-up materially reduces wrong-edit risk.
3. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
4. Cite source on changes. When proposing an edit, cite the specific path/to/file.ext:Lstart-Lend you're reasoning about, the same way RepoPilot's own RAG cites code in https://repopilot.app/r/TheAlgorithms/Rust.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live TheAlgorithms/Rust repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/TheAlgorithms/Rust.

What it runs against: a local clone of TheAlgorithms/Rust — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in TheAlgorithms/Rust | Confirms the artifact applies here, not a fork | | 2 | License is still MIT | Catches relicense before you depend on it | | 3 | Default branch master exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 41 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of TheAlgorithms/Rust. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/TheAlgorithms/Rust.git
#   cd Rust
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of TheAlgorithms/Rust and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "TheAlgorithms/Rust(\\.git)?\\b" \\
  && ok "origin remote is TheAlgorithms/Rust" \\
  || miss "origin remote is not TheAlgorithms/Rust (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(MIT)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"MIT\"" package.json 2>/dev/null) \\
  && ok "license is MIT" \\
  || miss "license drift — was MIT at generation time"

# 3. Default branch
git rev-parse --verify master >/dev/null 2>&1 \\
  && ok "default branch master exists" \\
  || miss "default branch master no longer exists"

# 4. Critical files exist
test -f "Cargo.toml" \\
  && ok "Cargo.toml" \\
  || miss "missing critical file: Cargo.toml"
test -f "src/backtracking/mod.rs" \\
  && ok "src/backtracking/mod.rs" \\
  || miss "missing critical file: src/backtracking/mod.rs"
test -f "src/ciphers/mod.rs" \\
  && ok "src/ciphers/mod.rs" \\
  || miss "missing critical file: src/ciphers/mod.rs"
test -f "src/bit_manipulation/mod.rs" \\
  && ok "src/bit_manipulation/mod.rs" \\
  || miss "missing critical file: src/bit_manipulation/mod.rs"
test -f ".github/workflows/build.yml" \\
  && ok ".github/workflows/build.yml" \\
  || miss "missing critical file: .github/workflows/build.yml"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 41 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~11d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/TheAlgorithms/Rust"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

Computed from the actual import graph (no LLM). Read in this order to learn the codebase from the foundation up — each step builds on the previous ones.

1. src/sorting/sort_utils.rs — Foundation: doesn't import anything internally and is imported by 3 other files. Read first to learn the vocabulary.
2. src/math/sieve_of_eratosthenes.rs — Foundation: imported by 2, no internal dependencies of its own.
3. src/ciphers/mod.rs — Built on the foundation; imported by 1 downstream file.
4. src/conversions/mod.rs — Built on the foundation; imported by 1 downstream file.
5. src/math/mod.rs — Layer 2 — composes lower-level code into reusable abstractions (imported 4×).
6. src/dynamic_programming/word_break.rs — Layer 3 — application-level code that wires the lower layers together.
7. src/dynamic_programming/mod.rs — Layer 4 — application-level code that wires the lower layers together.

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.