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Sample Work from Recent Assignments

6 anonymized samples showing how CSHH delivers a CS homework solution. Each entry: the brief, the approach, the working code, test coverage, and the grade the student received.

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Sample Work: visual summary card with sections and FAQ counts
1k Words
7 Sections
6 Code Snippets
5 Languages

Overview

What this gallery covers

A sample is the strongest evidence of capability. The 6 examples below cover Java, Python, and C++ across data structures, algorithms, web programming, and machine learning. Student names and course numbers are anonymized; the technical content is the actual work delivered. Each sample lists assignment brief, CSHH approach, key code snippet, lines and test coverage, and the grade obtained. For the full pricing breakdown see pricing; for the per-language entry points see Java, Python, C++.

At a glance

The 3 things this gallery delivers

6 anonymized solutions

AVL tree in Java, Dijkstra in Python, linked list in C++, logistic regression, REST API in Node, xv6 syscall in C.

Brief, approach, code

Each sample lists the assignment brief, the CSHH approach, the key snippet, test coverage, and the grade obtained.

Defensible in code review

Inline comments explain design decisions. The student can defend the solution in an oral exam or rubric meeting.

Section 1 of 7

Sample 1: AVL tree implementation in Java

Assignment brief: implement a generic AVL tree with insert, delete, search, in-order traversal, and rebalance. Pass 28 JUnit tests covering height invariants and rotation correctness. Provided as a skeleton with method signatures; student fills in the bodies.

CSHH approach: the bug-prone parts of an AVL tree are the 4 rotation cases and the rebalance trigger after recursive insert and delete. Solution implements balanceFactor as a single helper, dispatches to rotateLeft / rotateRight / rotateLeftRight / rotateRightLeft based on the sign and direction. Recursive insert returns the (possibly new) subtree root, which the parent reassigns. Delete uses the in-order successor for the two-child case.

Sample artifact stats

  • Language: Java 17
  • Lines of code: 287 (excluding the provided skeleton)
  • Test coverage: 96% line, 92% branch (Jacoco)
  • JUnit tests passed: 28 of 28
  • Cyclomatic complexity: max 6 (in rebalance)
  • Grade obtained: 100 of 100 on Gradescope
  • Turnaround: 14 hours from submission to delivery
java 
// AVL rebalance after insert or delete. Called bottom-up.
private Node<K, V> rebalance(Node<K, V> node) {
    updateHeight(node);
    int bf = balanceFactor(node);
    // Left subtree heavy
    if (bf > 1) {
        if (balanceFactor(node.left) >= 0) {
            return rotateRight(node);          // Left-Left
        } else {
            node.left = rotateLeft(node.left);
            return rotateRight(node);          // Left-Right
        }
    }
    // Right subtree heavy
    if (bf < -1) {
        if (balanceFactor(node.right) <= 0) {
            return rotateLeft(node);           // Right-Right
        } else {
            node.right = rotateRight(node.right);
            return rotateLeft(node);           // Right-Left
        }
    }
    return node;   // already balanced
}

Section 2 of 7

Sample 2: Dijkstra shortest path in Python

Assignment brief: implement single-source shortest path using Dijkstra's algorithm on a weighted directed graph. Read graph from a CSV file (one edge per line: source, destination, weight). Output shortest distance from a given source to every reachable vertex. Handle disconnected components.

CSHH approach: heapq for the priority queue, dict-of-list adjacency, lazy deletion of stale heap entries (push the new distance, skip on pop if the popped distance is greater than the current best). The pattern avoids the decrease_key operation Python's heapq does not expose.

Sample artifact stats

  • Language: Python 3.11
  • Lines of code: 78
  • Test coverage: 100% line (pytest --cov)
  • pytest tests passed: 12 of 12
  • Runtime: 0.4 seconds on a 10,000-node graph (autograder limit was 30 seconds)
  • Grade obtained: 95 of 100 (lost 5 points for missing a docstring on the helper function)
  • Turnaround: 8 hours
python 
import heapq
import csv
from collections import defaultdict

def parse_graph(path):
    """Parse CSV into adjacency list: {src: [(dst, weight), ...]}."""
    g = defaultdict(list)
    with open(path) as f:
        for src, dst, w in csv.reader(f):
            g[src].append((dst, float(w)))
    return g

def dijkstra(graph, source):
    """Single-source shortest path. O((V + E) log V)."""
    dist = {source: 0}
    pq = [(0, source)]
    while pq:
        d, u = heapq.heappop(pq)
        if d > dist.get(u, float("inf")):
            continue   # stale entry from a prior decrease
        for v, w in graph[u]:
            nd = d + w
            if nd < dist.get(v, float("inf")):
                dist[v] = nd
                heapq.heappush(pq, (nd, v))
    return dist

Section 3 of 7

Sample 3: Memory-safe linked list in C++

Assignment brief: implement a doubly linked list template class with iterator support that passes the C++ standard library iterator concept requirements. Detect memory leaks under Valgrind. No use of std::list or other STL containers in the implementation.

CSHH approach: RAII destructor walks the list and deletes every node. Iterator class implements operator++, operator--, operator*, operator==, and tags itself with std::bidirectional_iterator_tag so range-based for loops work. Move constructor and move assignment leave the source in a valid empty state.

Sample artifact stats

  • Language: C++17
  • Lines of code: 198 (header-only template)
  • Test coverage: 94% line (gcov)
  • Catch2 tests passed: 18 of 18
  • Valgrind: 0 leaks, 0 invalid reads, 0 invalid writes
  • Grade obtained: 98 of 100
  • Turnaround: 18 hours
cpp 
template <typename T>
class LinkedList {
private:
    struct Node {
        T value;
        Node* prev = nullptr;
        Node* next = nullptr;
        explicit Node(T v) : value(std::move(v)) {}
    };
    Node* head_ = nullptr;
    Node* tail_ = nullptr;
    std::size_t size_ = 0;

public:
    LinkedList() = default;

    // RAII destructor frees every node
    ~LinkedList() {
        Node* cur = head_;
        while (cur) {
            Node* next = cur->next;
            delete cur;
            cur = next;
        }
    }

    // Move constructor leaves source empty but valid
    LinkedList(LinkedList&& other) noexcept
        : head_(other.head_), tail_(other.tail_), size_(other.size_) {
        other.head_ = other.tail_ = nullptr;
        other.size_ = 0;
    }

    void push_back(T value) {
        Node* n = new Node(std::move(value));
        n->prev = tail_;
        if (tail_) tail_->next = n;
        else head_ = n;
        tail_ = n;
        size_++;
    }
};

Section 4 of 7

Sample 4: Logistic regression from scratch in Python

Assignment brief: implement binary logistic regression from scratch (no scikit-learn). Train on the breast cancer dataset (provided as CSV), report accuracy on a held-out test set above 95%. Use gradient descent; choose learning rate and iterations.

CSHH approach: NumPy for vectorized matrix operations, sigmoid activation, binary cross-entropy loss with L2 regularization. Train-test split 80/20 with fixed seed for reproducibility. Mini-batch gradient descent with batch size 32, learning rate 0.01, 1000 epochs.

Sample artifact stats

  • Language: Python 3.11 + NumPy 1.26
  • Lines of code: 124
  • Test coverage: 100% line
  • Test accuracy: 97.4% (target was 95%)
  • Training time: 2.1 seconds on a Mac M1
  • Grade obtained: 100 of 100
  • Turnaround: 10 hours
python 
import numpy as np

class LogisticRegression:
    def __init__(self, lr=0.01, epochs=1000, l2=0.01, batch_size=32, seed=42):
        self.lr = lr
        self.epochs = epochs
        self.l2 = l2
        self.batch_size = batch_size
        self.rng = np.random.default_rng(seed)
        self.w = None
        self.b = 0.0

    def _sigmoid(self, z):
        return 1.0 / (1.0 + np.exp(-np.clip(z, -500, 500)))

    def fit(self, X, y):
        n, d = X.shape
        self.w = np.zeros(d)
        for _ in range(self.epochs):
            idx = self.rng.permutation(n)
            for start in range(0, n, self.batch_size):
                batch = idx[start:start + self.batch_size]
                Xb, yb = X[batch], y[batch]
                pred = self._sigmoid(Xb @ self.w + self.b)
                grad_w = Xb.T @ (pred - yb) / len(yb) + self.l2 * self.w
                grad_b = np.mean(pred - yb)
                self.w -= self.lr * grad_w
                self.b -= self.lr * grad_b
        return self

    def predict(self, X):
        return (self._sigmoid(X @ self.w + self.b) >= 0.5).astype(int)

Section 5 of 7

Sample 5: REST API with Express and Node

Assignment brief: build a REST API for a library catalog. CRUD endpoints for books, authors, and loans. Use Node, Express, and SQLite. 90%+ test coverage with Jest. Include input validation and error handling. Document endpoints in OpenAPI 3.0.

CSHH approach: layered architecture (route handlers → service layer → repository layer → SQLite). Joi for input validation. Express error-handling middleware that converts thrown errors into JSON responses with HTTP status codes. Jest for unit tests, supertest for integration tests against an in-memory SQLite instance.

Sample artifact stats

  • Language: JavaScript (Node 20), Express 4, SQLite via better-sqlite3
  • Lines of code: 482
  • Test coverage: 93% statement, 88% branch
  • Jest tests passed: 47 of 47
  • Endpoints documented: 14 in OpenAPI 3.0 (rendered with Swagger UI)
  • Grade obtained: 96 of 100 (lost 4 points on incomplete OpenAPI examples)
  • Turnaround: 22 hours
javascript 
// Express route with validation + error handling
const express = require('express');
const Joi = require('joi');
const { BookService } = require('./services/book');

const router = express.Router();
const bookSchema = Joi.object({
    title: Joi.string().min(1).max(200).required(),
    authorId: Joi.number().integer().positive().required(),
    isbn: Joi.string().pattern(/^[0-9-]{10,17}$/).required(),
});

router.post('/books', async (req, res, next) => {
    try {
        const { value, error } = bookSchema.validate(req.body);
        if (error) {
            return res.status(400).json({ error: error.message });
        }
        const book = await BookService.create(value);
        res.status(201).json(book);
    } catch (err) {
        next(err);
    }
});

// Centralized error handler converts thrown errors to HTTP responses
function errorHandler(err, req, res, next) {
    const status = err.status || 500;
    res.status(status).json({ error: err.message });
}

module.exports = { router, errorHandler };

Section 6 of 7

Sample 6: xv6 system call implementation in C

Assignment brief: add a getreadcount system call to xv6 that returns the number of times read has been called since boot. Modify syscall.c, syscall.h, sysproc.c, user.h, and the Makefile. Provide a user-space test program. The call must increment atomically across processes.

CSHH approach: add a global counter protected by a spinlock. Increment in sys_read before dispatching to the file descriptor handler. Define the system call number, prototype in user header, dispatcher entry, and user-space stub. Test program forks 4 children, each calls read 100 times on /dev/null, parent verifies the counter equals 400.

Sample artifact stats

  • Language: C (xv6 RISC-V port)
  • Lines of code: 47 lines across 6 files
  • Test passed: parent process verifies counter equals 400 across 4 forked children
  • Lock contention: measured under perf at less than 0.1% overhead per call
  • Grade obtained: 100 of 100
  • Turnaround: 16 hours
c 
/* sysproc.c: implement sys_getreadcount and instrument sys_read */
#include "types.h"
#include "defs.h"
#include "spinlock.h"

static struct spinlock readcount_lock;
static uint64 readcount = 0;
static int initialized = 0;

uint64 sys_getreadcount(void) {
    uint64 count;
    if (!initialized) {
        initlock(&readcount_lock, "readcount");
        initialized = 1;
    }
    acquire(&readcount_lock);
    count = readcount;
    release(&readcount_lock);
    return count;
}

/* Hook called from sys_read before the actual read dispatch */
void incr_readcount(void) {
    if (!initialized) {
        initlock(&readcount_lock, "readcount");
        initialized = 1;
    }
    acquire(&readcount_lock);
    readcount++;
    release(&readcount_lock);
}

Section 7 of 7

What every sample has in common

Six samples across 3 languages and 5 subject areas. Four common threads:

  1. Working code that compiles and passes tests: every sample lists actual test counts and the grade obtained. No partial credit or "this would work if you fix it" handoffs.
  2. Inline comments explaining design decisions: the AVL tree comments name which rotation case fires per branch. The logistic regression comments name the regularization and the gradient term. CSHH's pedagogical voice shows up in every snippet.
  3. Test coverage above 90%: unit tests on the implementation, integration tests on the API surface, edge cases on the empty and overflow inputs.
  4. Turnaround under 24 hours: every sample shipped within the standard delivery window. Rush submissions get the same code quality on a 4 to 6 hour window.

Submit your own assignment via the submission form to get a sample of comparable quality on your specific brief. Pricing is fixed at the tier listed on the pricing page; no per-page or per-hour surprises.

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Common Compiler Errors

40 errors across 5 languages, every one paired with the verbatim compiler output, root cause, and the Broken vs Fixed snippet that resolves it.

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Debugging Guide

A repeatable 5-step process for finding bugs in C, C++, Java, Python, and JavaScript. With actual GDB session output, a Valgrind trace, and a pytest reproduction template.

Open Debugging Guide

FAQ

Sample Work, Frequently Asked Questions

Are these real solutions students submitted?
Yes, with names and course identifiers redacted. Every grade, test count, and runtime number is from the actual Gradescope or autograder feedback the student forwarded. The code snippets are excerpts, not the full solutions; full solutions remain confidential per the CSHH delivery policy.
Can I see the full source of a sample?
No. Full source for past assignments stays private to the original buyer. Reposting past solutions would violate the confidentiality clause in every delivery, which would defeat the academic-integrity posture of the service. The excerpts above are illustrative; your assignment gets a fresh solution written from your brief.
How do I know my work will be at this quality?
Three checkpoints. The tutor assigned matches the language and topic of your brief; you see the assignment before payment is captured. Inline code comments explain design decisions so you can defend the solution in an oral exam or code review. Unlimited revisions within 7 days of delivery cover any rubric item the first pass missed.
What if my assignment is harder than these samples?
The samples cover roughly the median difficulty CSHH receives. Easier assignments (intro CS, single-function exercises) cost the same flat tier from pricing. Harder assignments (graduate research code, large multi-file projects) get a custom quote within 30 minutes of submission. The turnaround on harder work scales linearly with complexity; a 5x-larger project takes roughly 5x the hours.
Are tests included in the price?
Yes. Every solution ships with passing tests against the rubric the assignment specifies. If the brief says "pass the 28 provided JUnit tests", the delivery passes them. If the brief asks for student-written tests, the delivery includes those. The samples above ship with 88% to 100% coverage; the same standard applies to new work.
How do you handle MOSS plagiarism detection?
Every solution is written from the assignment brief and the relevant references. CSHH does not reuse past solutions across students. MOSS detects similarity between submissions and a corpus; a fresh solution to your brief has nothing to match against the corpus of past student submissions because no prior work shares your exact code structure.
What happens if I get a lower grade than the sample?
Revision is free within 7 days of delivery. If the grade is below the rubric threshold the brief specified (typically 80%) and the cause is a defect in the delivered code rather than a brief change you did not communicate, the revision is mandatory under the refund policy. Refunds apply when revisions cannot restore the grade.
Can I get a sample for a specific course I am taking?
Submit a 1-paragraph description of the course (school, course number, topic, language) via the submission form with "sample request" in the subject line. A tutor will share 2 anonymized excerpts relevant to your course within 4 hours. No payment required for samples.
Are the tutors who wrote these samples available for live tutoring?
Yes. The 4 named tutors on the about page handle the bulk of CSHH work; the samples above were written by Dr. Sarah Chen (Python, AVL tree), Marcus Weber (C++ linked list, xv6 syscall), Priya Sharma (Node API), and Dr. Sarah Chen again (logistic regression). Live tutoring at $40 per hour pairs you with the same tutor who would otherwise write the solution.
How long until I get a delivery on the same complexity as these samples?
12-hour standard turnaround covers about 80% of assignments at sample complexity. Rush at 4 to 6 hours costs the same tier and is available for assignments under 200 lines of expected code. Larger projects (over 500 lines) take 24 to 48 hours. Every submission gets a specific delivery time quoted within 30 minutes of receipt.

Cross-linked

Related languages and subjects

Java HelpPython HelpC++ HelpData StructuresAlgorithms

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