CS 43: Computer Networks — Fall 2026

Class: Monday/Wednesday, 10:30 AM - 11:45 AM, Singer 222

Professor: Dr. Ranysha Ware

Office: Martin 338

Office hours: Monday 3:00 PM - 4:30 PM (Martin 328), Thursday 1:00 PM-2:00 PM (Martin 338)

Q&A Forum: EdSTEM

GitHub: Swarthmore GitHub Enterprise

iClickers: Clicker Registration

Grades: Grades will be posted on Moodle and Gradescope

Textbook: Accessible via TAP+ on Moodle

Weekly Lab Sessions: Martin 327

Welcome to CS 43. This course will introduce fundamental ideas in computer networks. The structure of this course may be different from many other CS courses at Swarthmore. We’ll be using a teaching model called peer instruction, which places a strong emphasis on classroom discussion and student interaction.

The course is composed of the following:

By the end of the course, we hope that you will have developed the skills to:

Please familiarize yourself with the course’s partnership expectations before starting the lab. See the lab policy for details.

All labs are due 11:59 pm ET on the due dates shown. Labs are due on Monday evenings.

Quizzes are closed book and will be 15-20 minutes covering the content from the previous week. See the quiz policy for details.

This is a tentative schedule; it may change as we go. You should read the assigned sections before class to prepare for class discussion points. All readings refer to the Kurose and Ross textbook unless otherwise indicated (e.g., there’s a link to some other source).

To access the recordings, you’ll need to be signed in to your @swarthmore.edu Google account.

Diversity, inclusion, and a mutual sense of belonging are all core values of this course. All participants in this course must be treated with respect by other members of the Swarthmore CS community. We must all strive, students and faculty both, to never make anyone feel unwelcome or unsafe in any way. Violations of these principles are viewed as unacceptable, and we take them very seriously. If you ever feel discriminated against or otherwise excluded, no matter how minor the offense, we encourage you to reach out to Prof. Ware or campus non-discrimination contacts.

The only effective way to learn the material and consistently do well in the labs is to get into the regular habit of: Discussing the top-down design of your code with your lab partner, before you start coding. Writing descriptive function comments as you go along. ** Incrementally coding and testing with GDB and Valgrind.

+ This process, will make you a sought after computer scientist once you graduate.

Academic honesty is required in all your work. Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. This includes, but is not limited to, obtaining solutions from students who previously took the course or code that can be found online. You may not share solutions after the due date of the assignment or make them publicly available anywhere (e.g. public GitHub repository).

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read anyone else’s code or let anyone else read your code. All code and written homeworks you submit must be your own with the following permissible exceptions: code distributed in class, code found in the course text book, and code worked on with an assigned partner. In these cases, you should always include detailed comments at the top of your files indicating on which parts of the assignment you received help, and what your sources were.

Generative AI (e.g., ChatGPT, Claude, GitHub Copilot, or other variants): Unless a specific assignment states otherwise, you may not use generative AI to produce any part of your submitted work. This is considered to be unauthorized collaboration with another source and is a violation of our academic integrity policy. This includes writing, completing, translating, or debugging code you submit, and drafting written answers. Editor autocomplete features powered by AI must be turned off while working on lab assignments.

You may use search engines or reference manuals to look up syntax, explain a concept, interpret a man page, or understand an error message. Search engines now display AI-generated summaries automatically; reading one that appears in your search results is not a violation. Prompting an AI tool with your code, your error output, or an assignment question is.

I will meet with each lab pair atleast once during the semester to talk through your code. You should both be able to explain how your implementation works and why you made the choices you did.

Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook:

The spirit of this policy applies to all course work, including code, homework solutions (e.g., proofs, analysis, written reports), and exams. Please contact Prof. Ware if you have any questions about what is permissible in this course.

Students must strictly adhere to the following policy, which applies to all quizzes and exams taken in a Computer Science course at Swarthmore:

Exam takers must place all non-essential items at the front of the room (or other designated area). Unless otherwise permitted, students may not have any electronic devices or course materials in their possession during the entirety of the exam. This includes cell phones, tablets, laptops, smart watches, course notes, articles and books, among others. These items should be placed at the front of the room near the proctor. If you need to leave the room during the exam, you must obtain permission from an instructor first. Any non-permitted discussion or aide in regards to exam material will result in immediate forfeiture of the exam and a report to the College Judiciary Committee. Please discuss any concerns or accommodations with your instructor prior to starting the exam.

If you believe you need accommodations for a disability or a chronic medical condition, please visit the Student Disability Services website for details about the accommodations process. Since accommodations require early planning and are not retroactive, contact Student Disability Services as soon as possible. You are also welcome to contact Prof. Ware privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services.

To receive an accommodation for a course activity you must have an official Accommodations Letter and you need to meet with Prof. Ware to work out the details of your accommodation at least two weeks prior to any activity requiring accommodations.

You are also welcome to contact Prof. Ware, privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services