Computer Science
Assignment Help
Clean, commented, executable code delivered before your deadline — written by developers who have taken the same courses, not AI generators or generic writers.
def binary_search(arr, target):
“””Returns index of target, or -1 if not found.”””
left, right = 0, len(arr) – 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid – 1
return –1
# Test the function
sorted_arr = [2, 5, 8, 12, 16, 23, 38]
print(f”Index: {binary_search(sorted_arr, 23)}”) # → 5
Theoretical Foundations
Meet Practical Code
Computer Science assignments sit at the intersection of rigorous mathematical reasoning and precise practical implementation. A sorting algorithm question is not just about getting an array sorted — it is about demonstrating understanding of time complexity, space trade-offs, and why one approach outperforms another in a given context. A database assignment is not just about writing a working SQL query — it is about schema design decisions, normalization rationale, and query optimization logic that your instructor is evaluating.
This dual nature is why CS students find assignments uniquely demanding: you can understand the theory in lecture and still fail to translate it into working, correctly structured code. Conversely, you can write code that appears to work on your test cases and still lose points because it does not follow the design principles the assignment rubric is assessing.
Our developers are not writers who learned to code. They are software engineers and computer scientists who also write. When you submit your assignment brief, your work goes to someone who has personally written merge sort implementations, built relational database schemas from ER diagrams, configured socket connections for networking assignments, and debugged segmentation faults at 2 AM. That practical depth is what produces code that is both technically correct and academically defensible.
What “Academically Defensible” Code Means
Most students never think about this until the moment their professor asks them to walk through their code during a lab check or viva. Code that works but was written without a clear logical structure — variable names like x1, temp2, zero comments, all logic jammed into a single function — is technically functional but academically indefensible. It signals to the grader that the student did not write it or does not understand it.
We write code that follows the conventions of the language it is written in, uses descriptive variable and function names, breaks complex logic into well-named helper functions, includes inline comments explaining non-obvious decisions, and comes with documentation that explains the architectural choices made. You can explain every line because every line has a reason.
Every Language.
Every Framework.
We cover the full academic technology stack — from introductory Python to advanced distributed systems. Here is a breakdown of what each area includes.
Python Programming
From beginner scripts to advanced data pipelines. Python is the most-requested language we work with, covering every academic context it appears in.
- ✓ OOP in Python (classes, inheritance, decorators)
- ✓ Data analysis (Pandas, NumPy, Matplotlib)
- ✓ File I/O, regex, API calls
- ✓ Flask and Django web applications
- ✓ Automation and scripting
Java Development
Java is the dominant language in undergraduate OOP curricula and enterprise software engineering courses. We write Java the way Horstmann or Bloch would — clean, idiomatic, and structured.
- ✓ OOP (encapsulation, polymorphism, inheritance)
- ✓ Collections framework, generics, lambdas
- ✓ GUI applications (JavaFX / Swing)
- ✓ Exception handling and file I/O
- ✓ Multithreading and concurrency
C++ & C Systems
Low-level systems programming in C and C++ demands precision with memory, pointers, and performance. We handle the assignments that make most students reach for Stack Overflow and still come up empty.
- ✓ Pointers, dynamic memory, smart pointers
- ✓ STL (vectors, maps, queues)
- ✓ Templates and operator overloading
- ✓ Linked lists, stacks, trees from scratch
- ✓ C-level system calls and file descriptors
C# & .NET
C# assignments in software engineering and game development programs. We handle both Windows application development and Unity game logic scripting.
- ✓ ASP.NET Core web applications
- ✓ LINQ and Entity Framework
- ✓ Windows Forms and WPF GUIs
- ✓ Unity game mechanics and scripting
- ✓ Async/await and Task Parallel Library
JavaScript & TypeScript
Modern web development assignments across the full stack. We cover both vanilla JS fundamentals and modern framework-based applications.
- ✓ DOM manipulation and event handling
- ✓ React, Vue, Angular SPAs
- ✓ Node.js and Express REST APIs
- ✓ Async JavaScript (Promises, async/await)
- ✓ TypeScript typing and interfaces
R & MATLAB
Statistical computing and numerical analysis assignments common in CS adjacent programs (Data Science, Bioinformatics, Engineering).
- ✓ R (ggplot2, dplyr, tidyverse, statistical models)
- ✓ MATLAB matrix operations and simulation
- ✓ Hypothesis testing and regression analysis
- ✓ Signal processing and image analysis
Data Structures
& Algorithm Analysis
DSA is the course that either makes or breaks a CS student’s confidence, and it is also the course that top technology companies use to evaluate candidates. Assignments here require not just a working implementation — they require you to prove runtime and space complexity using Big O notation, justify your choice of data structure, and often demonstrate that you understand why a particular algorithm performs the way it does on specific input distributions.
Data Structures We Implement
Our developers implement data structures from scratch in any language specified. This means no importing a library class and calling methods on it — we write the underlying Node class, the pointer logic, the resize operation for dynamic arrays, the rotation logic for AVL trees, and the hash function and collision resolution strategy for hash tables.
- Arrays, linked lists (singly, doubly, circular)
- Stacks and queues (array-based and linked list-based)
- Binary trees, BSTs, AVL trees, Red-Black trees
- Heaps (min-heap, max-heap) and priority queues
- Hash tables with chaining and open addressing
- Graphs (adjacency matrix, adjacency list) directed and undirected
- Tries and suffix arrays
- Disjoint set / Union-Find structures
Algorithm Categories
Beyond implementation, we write the formal complexity analysis that accompanies each assignment. Big O derivations are shown step by step, recurrence relations are solved using the Master Theorem where applicable, and space complexity analysis covers both auxiliary space and total space usage.
- Sorting: Bubble, Insertion, Selection, Merge, Quick, Heap, Counting, Radix
- Searching: Linear, Binary, Depth-First Search, Breadth-First Search
- Dynamic Programming: Memoization and tabulation approaches
- Greedy Algorithms: Interval scheduling, Huffman coding, Kruskal’s, Prim’s
- Graph Algorithms: Dijkstra’s, Bellman-Ford, Floyd-Warshall, Topological Sort
- Divide and Conquer: Closest pair of points, Strassen matrix multiplication
- Backtracking: N-Queens, Sudoku solver, subset sum
- String Algorithms: KMP, Rabin-Karp, LCS, edit distance
def lcs_length(s1, s2):
m, n = len(s1), len(s2)
dp = [[0]*(n+1) for _ in range(m+1)]
for i in range(1, m+1):
for j in range(1, n+1):
if s1[i–1] == s2[j–1]:
dp[i][j] = dp[i–1][j–1] + 1
else:
dp[i][j] = max(dp[i–1][j], dp[i][j–1])
return dp[m][n]
Database Design
& SQL Mastery
Database assignments test schema design logic as much as query syntax. We handle the full workflow from ER diagram to optimized query.
Database Design Process
A well-designed database assignment starts before a single line of SQL is written. The Entity-Relationship (ER) diagram must correctly identify entities, their attributes, primary and foreign keys, and the cardinality of each relationship (one-to-one, one-to-many, many-to-many). Getting this wrong cascades into normalization errors and query failures.
We produce ER diagrams in the notation specified by your course (Chen notation, Crow’s Foot, or UML), convert them to relational schemas correctly, and then normalize to the appropriate normal form — 1NF, 2NF, 3NF, or BCNF — with written justification at each step explaining what dependency was eliminated and why.
SQL Query Complexity Levels
- Basic SELECT, INSERT, UPDATE, DELETE with WHERE clauses
- Multi-table JOINs (INNER, LEFT, RIGHT, FULL OUTER, SELF)
- Aggregate functions with GROUP BY and HAVING
- Subqueries (correlated and non-correlated)
- Window functions (ROW_NUMBER, RANK, LAG, LEAD)
- Stored procedures, functions, and triggers
- Transaction control (COMMIT, ROLLBACK, SAVEPOINT)
- Index creation and query execution plan analysis
- Views and materialized views
NoSQL Databases
Beyond relational databases, we handle NoSQL assignments in MongoDB (document model, aggregation pipelines, indexing), Redis (key-value operations, pub/sub, TTL strategies), Cassandra (wide-column model, partition key design), and Neo4j (graph database queries with Cypher).
Database Systems Covered
- MySQL and MariaDB
- PostgreSQL (including JSON/JSONB columns)
- Oracle Database (PL/SQL)
- Microsoft SQL Server (T-SQL)
- SQLite (for embedded/mobile assignments)
- MongoDB Atlas and local deployments
SELECT
c.customer_id,
c.full_name,
SUM(o.total_amount) AS total_spend,
COUNT(o.order_id) AS order_count,
RANK() OVER(ORDER BY SUM(o.total_amount) DESC)
AS spend_rank
FROM customers c
INNER JOIN orders o
ON c.customer_id = o.customer_id
WHERE o.order_date >= DATEADD(year, –1, GETDATE())
GROUP BY c.customer_id, c.full_name
ORDER BY total_spend DESC
FETCH FIRST 5 ROWS ONLY;
Database Assignment Deliverables
Full-Stack Web
Development Projects
Web development assignments span a wide range — from a static HTML/CSS page in a freshman course to a full multi-page application with user authentication, a REST API, and a relational database in a senior capstone. We cover every level.
Frontend Development
HTML5 and CSS3 assignments covering semantic markup, responsive design with Flexbox and CSS Grid, accessibility standards, animations, and form validation. JavaScript assignments covering DOM manipulation, event-driven programming, fetch API calls, and framework-based development in React, Vue, or Angular.
Backend Development
Server-side development in Node.js with Express, Python with Django or Flask, Java with Spring Boot, and PHP. We build RESTful APIs with correct HTTP verb usage, status codes, error handling middleware, input validation, and JSON response formatting that matches your assignment specification.
Full-Stack Projects
When an assignment requires a complete application — user registration and login, CRUD operations on a resource, a responsive frontend consuming your own API, and a database backing everything — we build it as a coherent system, not a set of disconnected parts. The architecture is documented, the code is organized by industry-standard folder structure, and the README explains setup in enough detail that your TA can run it locally.
Operating Systems
& Networking Assignments
The two hardest practical courses in a CS degree. We have developers who have implemented schedulers, memory managers, and network protocol handlers from scratch.
Operating Systems Topics
FCFS, SJF (preemptive/non-preemptive), Round Robin, MLFQ — with Gantt charts, turnaround time, and waiting time calculations.
Paging, segmentation, page replacement algorithms (LRU, FIFO, Optimal, Clock), frame allocation, and TLB simulation.
Resource allocation graph construction, Banker’s algorithm implementation, deadlock detection and recovery strategies.
Bash scripts for file system operations, process management, cron jobs, piping, redirection, and system monitoring.
Mutex, semaphore, monitor implementations. Producer-Consumer, Reader-Writer, and Dining Philosophers problems solved in C/Java/Python.
Computer Networking Topics
TCP and UDP client-server applications in Python, Java, or C. Multi-threaded servers handling concurrent connections.
Dijkstra’s and Bellman-Ford as distance-vector and link-state routing protocols. Network topology graphs and routing table construction.
Building a minimal HTTP/1.1 server that parses requests, serves static files, handles error codes, and supports persistent connections.
Encryption fundamentals, RSA and AES implementation exercises, SSL/TLS handshake analysis, and firewall rule logic.
Packet capture labs, protocol header analysis, TCP three-way handshake documentation, and ARP/DNS trace interpretation.
Machine Learning
& AI Assignments
Machine learning assignments are now a standard part of upper-level CS programs and graduate data science curricula. The gap between knowing ML concepts from lecture and implementing them correctly in a Jupyter Notebook — with proper data preprocessing, model selection justification, evaluation metrics, and visualizations — is significant.
ML Pipeline Assignments
We implement end-to-end ML pipelines: loading and cleaning datasets, handling missing values and categorical encoding, feature scaling, train/test/validation split, model training, hyperparameter tuning, and evaluation using the metrics appropriate to the task (accuracy, precision, recall, F1, AUC-ROC for classification; RMSE, MAE, R² for regression).
Machine Learning Topics We Cover
- Supervised learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, K-Nearest Neighbors
- Unsupervised learning: K-Means clustering, DBSCAN, PCA, t-SNE dimensionality reduction
- Neural networks: Feedforward, CNN, RNN, LSTM using TensorFlow/Keras or PyTorch
- Natural Language Processing: tokenization, TF-IDF, sentiment analysis, transformer fine-tuning
- Reinforcement Learning: Q-learning and policy gradient basics
- Model evaluation: cross-validation, confusion matrices, ROC curves, learning curves
AI Theory Assignments
Beyond implementation, we handle theoretical AI assignments: search algorithm analysis (BFS, DFS, A*, iterative deepening), constraint satisfaction problems, game tree search (minimax, alpha-beta pruning), Bayesian networks, and logic-based AI with propositional and first-order logic exercises.
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
# Train with optimal hyperparameters (via GridSearchCV)
clf = RandomForestClassifier(
n_estimators=200,
max_depth=8,
class_weight=“balanced”, # handles imbalance
random_state=42
)
clf.fit(X_train, y_train)
# 5-fold cross-validation
scores = cross_val_score(clf, X, y, cv=5, scoring=“f1_macro”)
print(f”CV F1: {scores.mean():.3f} ± {scores.std():.3f}”)
ML Assignment Deliverables
- Jupyter Notebook (.ipynb) with markdown explanations
- Visualizations: confusion matrix, ROC curve, feature importance
- Model performance report with interpretation
- requirements.txt for reproducible environment setup
- Written analysis of results in your assignment format
From Assignment Brief
to Running Code
Submit Brief
Upload requirements, starter code, language specs, IDE, and deadline. The more detail, the better the match.
Developer Matched
Assigned to a developer with direct experience in your language, topic, and course level — not whoever is available.
Code Written & Tested
Executable, commented code written from scratch. Tested against the cases in your spec with output screenshots included.
Delivered & Defended
Files delivered before deadline. Detailed comments mean you can walk your instructor through every decision.
What Quality Code
Actually Looks Like
CS instructors grade code on multiple axes simultaneously: functionality (does it produce correct output?), design (is the structure logical and maintainable?), efficiency (is the algorithm appropriate for the problem scale?), and style (does it follow the language’s conventions and the course’s coding standards?).
Functional code that ignores design — a 200-line main() function with no helper methods, variable names like a, b, x, and no comments — will get docked on every rubric category except output correctness. We write code that scores well on all four axes.
Language-Specific Conventions We Follow
- Python: PEP 8 style guide, type hints where appropriate, f-strings, list comprehensions when idiomatic
- Java: Oracle naming conventions (camelCase methods, PascalCase classes), Javadoc, checked vs. unchecked exception handling
- C++: Rule of Three/Five, RAII pattern, const-correctness, avoiding raw pointers where smart pointers apply
- JavaScript: ES6+ syntax, async/await over callback chains, destructuring, module pattern
- SQL: Uppercase keywords, table aliasing, formatting multi-line queries readably, commenting complex logic
Human-Written, Not AI-Generated
We do not run your assignment through ChatGPT, Copilot, or any other code generation tool. Here is why that matters practically: AI-generated code fails on edge cases. It hallucinates method signatures. It writes code that appears correct but uses deprecated APIs or makes assumptions about input that do not hold. It cannot follow the specific naming conventions or architecture requirements your professor specified in the brief. Human developers, who have written the same type of assignment before, do not make those errors.
Checklist Before Delivery
Smart Academic Writing vs. Generic Coding Services
Transparent Project Pricing
Coding projects vary in complexity, so we price by scope rather than by page. Here is how we structure it.
- Debugging existing code
- Single function implementation
- Simple data structure
- Short automation scripts
- DSA assignments (any language)
- OOP class hierarchies
- Database schema + queries
- OS simulation (scheduler, memory)
- ML model training + report
- Full-stack web applications
- Capstone software projects
- Research-level ML pipelines
- Distributed systems prototypes
15% off your first order. Rush surcharges apply for 6-hour delivery. All prices include source code, comments, README, and output screenshots. Free revisions included.
Hire Expert Developers
Software engineers with graduate degrees who understand both academic requirements and real production code quality.
Eric Tatua
Expert in low-level programming in C and C++, operating systems concepts, embedded systems, and computer architecture. Handles the assignments most developers avoid.
Benson Muthuri
SQL expert and Python data science specialist. Builds complete database solutions from ER diagram to optimized queries. Also handles Tableau and Power BI assignments.
Dr. Michael Karimi
Algorithm theory and complexity specialist with research publications. Handles graduate-level DSA, formal proofs, ML project pipelines, and research-level computational problems.
Sofia Andreou
Full-stack developer specializing in React, Node.js, and REST API design. Builds complete web applications from scratch and handles framework-specific assignments in Vue and Angular too.
What CS Students Say
Eric fixed my C++ pointer errors in under 2 hours. The code compiled perfectly on the first try and the comments were so thorough that I could explain every line to my professor during the demo. Best $35 I ever spent.
I needed a complex SQL schema with normalization justification and 12 different queries for my database systems final. Benson delivered everything with a clean ER diagram, DDL scripts, and working queries. My TA was impressed.
My ML capstone had a deadline in 3 days and I was still stuck on data preprocessing. Dr. Karimi built the entire pipeline in Jupyter, added explanatory markdown cells, got 94% accuracy, and included a full analysis. Got an A.
Needed a Round Robin scheduler simulator in Java with Gantt chart output. Got clean OOP code, a proper README, and the Gantt chart rendered as console output. Exactly what the rubric asked for. Submitted 12 hours before the deadline.
The Complete Guide to Succeeding in Computer Science Assignments
This guide covers the actual practical realities of CS coursework at the undergraduate and graduate level — what instructors are looking for, where students consistently lose points, how different assignment types should be approached, and how to build the skills that make the coursework manageable.
Why CS Assignments Are Uniquely Demanding
Computer science assignments have an unusual property: they have a binary quality at one level (the code either runs or it does not) but a continuous quality spectrum at another (correct output from terrible code is not the same as correct output from well-structured code). This binary/continuous duality catches students who focus only on getting the output right and ignore every other dimension the instructor is grading.
At the binary level: if your code does not compile, you lose most of the assignment grade regardless of how well-designed it might have been. At the continuous level: a grader reading your code is evaluating readability, design choices, algorithm efficiency, adherence to the language’s conventions, and documentation quality — all simultaneously. Missing on any of these axes costs points on every assignment, and those deductions accumulate across a semester into a meaningful GPA impact.
Understanding What Computer Science Graders Actually Look For
Most CS professors and TAs grade coding assignments using a rubric that breaks down into roughly five categories. Understanding these helps you know where to invest your effort and where common mistakes happen.
Correctness (35–50% of most rubrics): Does the code produce the specified output for the provided test cases? Does it handle the edge cases mentioned in the assignment? This is the baseline — necessary but not sufficient for a high grade. Professors commonly include edge cases like empty arrays, negative numbers, null inputs, and maximum-size inputs specifically to catch implementations that only work on the obvious happy-path cases.
Efficiency (15–25%): For any algorithm assignment, is the time and space complexity appropriate? A bubble sort implementation where the assignment specified that the solution should work efficiently on large datasets will lose algorithmic efficiency points even if the sort produces correct output. The efficiency expectation is always relative to what the course has covered — you will not be penalized for not using Timsort when the course has only covered O(n log n) comparison sorts, but you will be penalized for an obviously avoidable quadratic solution when a linear one was straightforward.
Design and Structure (15–25%): Is the code organized into logical functions/methods? Does the class hierarchy (for OOP assignments) make sense? Are repeated logic blocks extracted into helpers, or is the same code copy-pasted three times? Is the overall architecture appropriate for the problem? This is where single-function programs and poorly decomposed solutions lose points even when they work correctly.
Documentation and Style (10–20%): Are there meaningful comments? Do variable and function names describe what they contain and do? Does the code follow the naming conventions specified for the course (camelCase for Java, snake_case for Python, etc.)? Is there a README or header comment explaining the program’s purpose, inputs, and outputs? This category is frequently underestimated by students and frequently undergraded by instructors who find undocumented code frustrating to read.
Testing (0–15%): Some assignments explicitly require test cases. Even where they do not, showing evidence of testing — either in a separate test file or through sample runs with multiple inputs — demonstrates thoroughness and often earns partial credit or bonus points.
The Debugging Process That Actually Works
Most CS students debug by staring at code until they see the problem. This works on simple assignments but fails catastrophically on complex ones where the bug is not visible by inspection. Professional developers use a structured debugging process, and learning it early makes every CS course easier.
The first step is understanding the error message completely before changing anything. Java’s NullPointerException, Python’s IndexError, C’s segmentation fault — each of these tells you something specific. A NullPointerException on line 47 means an object reference on line 47 is null; the question is why it became null. Reading the full stack trace (not just the first line) shows you the call chain that led to line 47, which tells you where the null was introduced.
The second step is forming a specific hypothesis before writing any code. “I think the bug is in the updateBalance method because it does not handle the case where the account has never been initialized” is a testable hypothesis. “Something is wrong with the account handling” is not. Write down your hypothesis, then design a test that would either confirm or refute it.
The third step is using print statements or a debugger to verify the state of your variables at specific points in the execution — not just at the final output, but at intermediate checkpoints. If you expect userList to contain 5 elements after the loading loop but it contains 3, the bug is in or before the loop, not in whatever happens with the list later.
Data Structures: Choosing the Right Tool
One of the most commonly tested skills in CS courses — and one of the most commonly failed — is choosing the appropriate data structure for a problem. The right data structure choice reduces algorithm complexity; the wrong one creates performance problems that cannot be fixed by optimizing the algorithm around it.
The selection framework is straightforward once you internalize it. If you need ordered data with random access by index, you want an array or ArrayList. If you need fast insertion and deletion at arbitrary positions, you want a linked list (though modern implementations often use dynamic arrays anyway). If you need LIFO (Last In First Out) access, you want a stack. If you need FIFO (First In First Out) access, you want a queue. If you need to look up values by key in O(1) time, you want a hash map. If you need ordered key-value storage with O(log n) operations, you want a balanced BST or TreeMap. If you need to find the minimum or maximum element quickly, you want a heap.
The graph data structure choice — adjacency matrix versus adjacency list — is driven by the density of the graph and which operations dominate. Adjacency matrices are O(1) for edge existence queries but O(V²) in space; adjacency lists are O(degree) for edge queries but O(V+E) in space and better for sparse graphs and traversal algorithms.
Database Design: The Normalization Process
Database normalization assignments follow a predictable pattern: given a poorly designed flat table or a set of functional dependencies, normalize to a specified normal form. Understanding the process procedurally makes these assignments systematic rather than guesswork.
First Normal Form (1NF) requires that every attribute in a relation is atomic (indivisible) and that there are no repeating groups. If a table has a column called “phone_numbers” that stores “555-1234, 555-5678”, it violates 1NF. The fix is to separate multi-valued attributes into separate rows or a separate table.
Second Normal Form (2NF) requires 1NF plus that every non-key attribute is fully functionally dependent on the entire primary key (not just part of it). Partial dependencies are only possible when the primary key is composite. If a table has primary key (StudentID, CourseID) but a non-key attribute like StudentName depends only on StudentID, that is a partial dependency and a 2NF violation. The fix is to decompose: move StudentName to a Students table where StudentID is the full key.
Third Normal Form (3NF) requires 2NF plus that no non-key attribute is transitively dependent on the primary key through another non-key attribute. If StudentID → DepartmentID and DepartmentID → DepartmentName, then StudentID → DepartmentName is a transitive dependency. The fix is to create a Departments table.
Approaching a Full-Stack Web Assignment
Full-stack assignments are intimidating because they have more moving pieces than any other type. The approach that prevents failure is working from the data layer up, not from the frontend down.
Start with the database schema. Define your entities, their attributes, and their relationships. Write the DDL. Insert sample data. This gives you a stable foundation that everything else is built on. Moving to the backend next — define your API endpoints, implement each route handler against your real database, and test every endpoint with a tool like Postman or curl before touching any frontend code. This isolation ensures that when frontend problems appear, you know the backend is correct. Only once the API is verified do you build the frontend, connecting it to your tested API endpoints.
The most common failure mode in full-stack assignments is building the frontend and backend simultaneously, encountering bugs that could belong to either layer, and spending hours debugging the wrong layer. Strict bottom-up development eliminates this confusion.
Operating Systems: Getting Scheduling Right
Process scheduling simulation assignments are among the most mechanical in CS — once you understand the algorithm, the implementation is a matter of careful bookkeeping. The failure mode is almost always incorrect tracking of time, waiting time, or turnaround time.
For every scheduling algorithm, the key tracking variables are: arrival time (given), burst time (given), start time (when did the process first get CPU time?), completion time (when did it finish?), turnaround time (completion time minus arrival time), and waiting time (turnaround time minus burst time). FCFS orders by arrival time and is non-preemptive. SJF orders by remaining burst time and is either preemptive (SRTF) or non-preemptive depending on the assignment specification. Round Robin requires a ready queue and a time quantum, cycling through processes and re-enqueueing any that have not completed when their quantum expires.
The Gantt chart is usually required and is a visual representation of which process held the CPU at each time unit. Building it incrementally as you simulate the scheduler — rather than trying to construct it from the final data — prevents the errors that come from trying to reconstruct a timeline after the fact.
Frequently Asked Questions
Answers to what CS students ask before placing their first order.