Lectures and Details (Winter 2024)


Canvas — while this course does not use Canvas, we still create a Canvas entry for this course which contains only links to key platforms used by this course. You can find the same information below too.

Piazza (need access? email EECS481Staff@gmail.com)

Autograder Homework Code Submission (need access? email EECS481Staff@gmail.com)

Gradescope Homework Report Submission (need access? email EECS481Staff@gmail.com)

Structured Activities — see the below "Attendance Policies" section to learn about how structured activities work.

Discussion Section Materials — all discussion sections are optional.

SSD approved extended exam time confirmation Google Form — you have to (1) first obtain approval from SSD and (2) then fill this form in order to get the extended exam time

Remote Office Hours Queue


Section 001 / 002 / 003 (Live Lecture) — MoWe 3:00-4:30pm — STAMPS (recorded via CAEN)

Primary Discussion Section — Fri 1:30 - 2:30 — 1014 DOW — led by Henry Beckstein (materials, recorded via CAEN)

Structured Activities — available to all students (materials)

(In-Person) Discussion Sections — all optional, all start Jan 15th. Note: all discussion sections are converted to in person OHs, except for the primary discussion section.

Thu 4:30-5:30 — 1008 FXB — Adit Kolli
Thu 5:30-6:30 — 1303 EECS — Henry Beckstein
Fri 9:30-10:30 — 1014 DOW — Max Liu
Fri 10:30-11:30 — 1014 DOW — Youcef Abella
Fri 11:30-12:30 — 1017 DOW — Xiangyu Zhou
Fri 12:30-1:30 — 1018 DOW — Arjav Patel
Fri 1:30-2:30 — 1014 DOW — Henry Beckstein
Fri 2:30-3:30 — 1014 DOW — Rui Dong


None (but see below, and a personal computer or laptop is required)

Course Staff
Adit Kolli (IA)
Alex Chong (Grader)
Antony Wu (Grader)
Arian Qoshi (IA)
Arjav Patel (GSI)
Emily Walker (Grader)
Fanghao Zhang (Grader)
Henry Beckstein (IA)
Jingyi Mao (Grader)
Livia Brahaj (IA)
Max Liu (IA)
Pranav Bhoopala (GSI)
Ritij Jutur (Grader)
Rohit Saripalle (IA)
Rui Dong (GSI)
Sreya Challa (Grader)
Wenxin Tian (Grader)
William Kong (Grader)
Xiangyu Zhou (GSI)
Xinyue Zhou (Grader)
Yang Gao (Grader)
Youcef Abella (IA)
Yunchi Lu (GSI)
Office Hours
(starting Jan 15th)

Office Hour Queue

See below Google Calendar for detailed hours

Professor Office Hours
(starting Jan 15th)
11am-12pm Wed
Ali (1031 DOW)
10am-11am Tue
Brendan (Cooley 2958B)
10am-10:45am Wed
Xinyu (4620 BBB)

Office Hour Calendar


This Course Considers ...

  • How can you get a patch accepted in a large software project?
  • Can we be confident that your code is correct?
  • What can be automated, and what is best done manually?
  • How can we measure software qualities?
  • How can we avoid problems early?

Advice From Former Students

Read what former students say about whether or not to take this course.

Successful software projects require more than just technical expertise. Figuring out what the client wants, collaborating in a team, managing complexity, mitigating risks, staying on time and budget, and determining, under various constraints, when a product is "good enough" to be shipped are equally important topics that often have a significant human component. This course explores these issues broadly and covers the fundamentals of modern software engineering and analysis.

This course focuses on software engineering and analysis. At a high level, the course is organized around five core topics:

  • overview Measurement and Risk
  • qa Quality Assurance (especially testing)
  • bugs Software Defects
  • design Software Design
  • coding Productivity at Scale
A culminating assignment involves making a contribution to an open source project: identifying an issue, understanding the local development process, and then actually fixing a bug or adding a feature, with extra credit awarded if your contribution is merged into the project.

This course is an upper-level CS technical elective for both CS-Eng and CS-LSA. (It does not show up in some versions of the CS-LSA Program Guide, but it does, in fact, count. Students can check their Audits to confirm.) It is not a capstone course or a major design experience course. It focuses on individual mastery of key software engineering concepts. It does not feature a large team project.

The expected workload for this class is "moderate" — one notch harder than UI Design, but two notches easier than Operating Systems.

Advice From Former Students

Read what former students had to say about how hard this course is.

What will I learn at the end?

Skimming a previous final project report can provide a perspective on the sorts of experiences associated with this class. A number of other final project reports are available.

This course draws inspiration from Carnegie Mellon's Foundations of Software Engineering (15-313) course as well as from the insights of Drs. Prem Devanbu, Christian Kästner, Marouane Kessentini, Kevin Leach, and Claire Le Goues.

Attendance Policies

In Person vs. Remote Participation

All sections (001, 002, 003) of this course share the same meeting location and time (see above), however, we provide two attendance modalities.

Note that, no matter which section you are officially enrolled in, you can always choose between these two participation modalities. Also, you can switch in between these two options across the entire semester: attending in person for lecture X and remote for lecture Y is totally fine. We encourage students to attend in person if possible so you can physically interact with you peers and instructors, but we also offer the flexibility to allow remote learning when that's preferred.

We note a few differences between in person and remote participation:

The rationale of providing two participation options is to maximize flexibility; we make this change based on student feedback from past semesters. This approach is slightly different from prior offerings, and we welcome feedback from students. The goal of providing this flexibility is to achieve better learning, and we trust students can choose the right combination of in person and remote based on their individual situations. In general, the in person option requires some overhead to travel to the classroom at the lecture time, while the remote counterpart might require some more time to attempt to solve an additional problem.

With the exception of lecture attendance and participation, all other aspects of the course are identical across sections. In particular, all students have equal access to lecture recordings, participation activities, discussion section materials, the same homeworks, the same exams, the same due dates (except for participation assignments), etc.

"Two Plus Infinite"

To reduce student stress and provide support for individual circumstances, each student may miss two (please read this whole sentence before worrying) graded participation activities (two in person, two remote, or one in person plus one remote) without excuse without penalty, and also infinite graded activities with approved excuses without penalty. You do not need to report or request anything for the first two; these will be applied automatically. Students with approved documentation can miss more activities, but to simplify things for students, you do not even have to explain to us why you are missing the first two. (Note that this applies to participation only and not for any other other course quiz or assignment or exam.)

If you are uncertain about whether your circumstances are an excused absence, email EECS481Staff@gmail.com with your information and documentation. The course staff will review your information and apply any accommodations merited by course policies. Your email will be read by a real human.

Discussion Sections

All discussion sections are in person only. They are not required, do not feature mandatory participation, and do not assess attendance. All discussion section materials are made available online. Recordings of the primary discussion section will be made available to all students, while the rest of discussion sections are turned to in-person office hours.

Waiting List

If you are currently on the waiting list but hope to take this course, you should choose the remote participation option until you're officially enrolled into one of the sections. This means, you should watch the lecture recordings offline and complete the participation check offline. This way, we have your participation record on file and we can give you participation credit should you later be officially added to the course. On the other hand, if you do not complete such participation checks, you will lose their points when you are officially enrolled. Basically, you should pretend you're taking the course remotely and complete all the required work, until you are officially part of the class.

Advice From Former Students

Read what former students say about lecture attendance.

Grading Breakdown

There are two exams during the semester. Both are delivered remotely. The grading breakdown is as follows:

  • 1% Homework 0 — Dev Setup
  • 10% Homework 1 — Test Coverage
  • 10% Homework 2 — Test Automation
  • 10% Homework 3 — Mutation Testing
  • 10% Homework 4 — Defect Detection
  • 10% Homework 5 — Debugging Automation
  • 15% Homework 6 — Contribution
  • 5% Comprehension Quizzes
  • 5% Participation and Professionalism
  • 12% Examination 1
  • 12% Examination 2

By default, this class has no curve. If we do implement a curve (which has not happened in the last few semesters), we only ever curve up. If we do implement a curve, it would be calculated at the end of the semester (and not on a per-each-assignment basis).

The exams are intentionally given a low weight to reduce student stress and the impact of a "bad day". Similarly, because there is no curve, students are never in competition with each other.

Advice From Former Students

Read what former students had to say about how to succeed in this course.

Homework Assignments

There are six homework assignments for this course. The assignments involve the electronic submission of artifacts. Some (e.g., test cases) are graded automatically and admit immediate feedback. Others (e.g., prose descriptions) are graded manually. For certain assignments it is possible to work as a team.

Advice From Former Students

Read what former students say about the first homework assignments.

Reading and Comprehension

A critical part of software engineering is reading — both code and prose. To encourage you to keep pace with the material, we will assign reading comprehension quizzes. These quizzes may consist of elements such as (1) comprehension questions about any readings, especially those new since the last quiz; (2) questions about the lecture materials; (3) a 4-sentence summary of lecture material; or (4) a random code word shared during a lecture. The goal is to encourage engagement and retention.

For on-line reading comprehension quizzes, you will have a 72 hour span near the associated lecture to begin your reading quiz. Quizzes are announced on Gradescope. Once you begin the quiz, you will typically have 10 minutes to complete it. The quizzes are designed to be completable in 5 minutes, but additional time is given as a blanket accommodation. The quizzes are "open notes" (but are typically constructed to favor completing the reading before starting the quiz).

Software engineering is often more engineering than science: the basic concepts may be easy to grasp, but the trouble is found in the details. Questions such as "which of these methods works best in the real world?" and "what are successful companies actually doing?" are paramount. As a result, many of the readings are experience reports from companies (e.g., Microsoft, Google, etc.) or academic papers (e.g., with human studies). We have structured this course so that there is no expensive textbook and all of the readings are available on-line for free.

Some of the readings are marked optional. Next to each such optional reading is a small "advertisement" for it. The optional readings are not required for any class assignments, but there may be extra credit questions on exams or quizzes that reference them.

Advice From Former Students

Read what former students had to say about comprehension quizzes and examinations.

Late Assignment Policy

EECS 481 does not feature a generous late policy. Assignments, quizzes or exams turned in late typically receive zero points. (In some extenuating cases you may receive h% off, where h is the number of hours late.)

In other classes, late policies may be more lenient (e.g., students may receive a number of fungible "late days", etc.). EECS 481 is different because scheduling and risk for projects are explicitly topics in this course (they are covered in lecture and in the readings, etc.). Staying on schedule is part of the material for the course, and is thus part of the assessment. If you are working for a company that is shipping software by a particular date and you miss that deadline, your contribution will not be included.

To support students, all of the course materials, assignments and due dates are provided on the first day of class. No due dates are ever shifted to be earlier. You can access the autograder for any assignment as early as the first day of class. The freedom and responsibility rest with you. (For example, if you know that you struggle with deadlines, or if your other classes have exams around a particular point, you may want to "pretend" that an assignment in this class is due earlier than it actually is. If you miss your internal deadline by a bit, you can still make the official deadline. If you are used to courses where there are frequent email updates or calendar reminders, this is a good opportunity to practice setting up your own reminders for future situations in which organizational ones may not be available.)

All course materials submitted for a grade must be turned in by midnight on the last date listed on the course syllabus.

Regrade Policy

Regrade requests for exams, assignments, or written assignments must be received within one week of you receiving your score. All regrade requests should be made via Gradescope if possible (if a request absolutely cannot be made via Gradescope for a certain assignment or exam, please email it privately to the course staff). When we regrade an assignment we will look over it very carefully for correctness: it is possible that after a regrade you will end up with fewer points than before the regrade. Regrades should be treated with caution and used only when the graders have made a clear mistake evaluating your work.

The Waiting List, Course Permission, Overrides

Historically, the single most common student question about this class relates to the waiting list. The question is some variant of (or logically reduces to) "I am currently at position X on the waiting list; am I likely to get in to the class before the deadline?"

Now, for the most common student question:

Unfortunately, I have no special insight here (i.e., it has never happened that a student has said to me "I plan to drop in X days but won't drop today", so I do not have any secret information about the plans of other students to drop or not). The best we can do is use the past to predict the future (see raw data above).

Student Mental Health

Students often experience strained relationships, increased anxiety, alcohol or drug problems, feeling down, difficulty concentrating, family issues, or a lack of motivation. Student mental health concerns are quite common but we don't always talk about them. The University of Michigan is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, or in need of support, confidential mental health services are available on campus.


Your class work might be used for research purposes. For example, we may use anonymized student assignments to design algorithms or build tools to help programmers. Any student who wishes to opt out can contact the instructor or teaching assistant to do so after final grades have been issued. This has no impact on your grade in any manner.

Students interested in considering undergraduate research should make an appointment to talk about it. I am happy to discuss independent study projects, senior projects, paid research work over the summer, research work for credit, and graduate school.