CSE 331: Syllabus

Students are expected to be familiar with all of the information below. Read it fully and carefully.

Course Structure

Prerequisites

The course assumes knowledge of the Java programming language at the level covered in CSE 123. Among other things, we assume that all students are familiar with writing recursive methods and with data structures such as binary search trees.

Goals

The goals of this course are to teach students to:

• Organize software so that it is easy to test, understand, and change.
• Write precise specifications for individual components so that the only implementations satisfying the specification are the ones intended.
• Understand which bugs are caught by the type system and which are not.
• Choose test cases that meet industry standards for coverage.
• Ensure that implementations are fully correct for all inputs through reasoning.
• Practice defensive programming so that any bugs not caught by the type system, testing, or reasoning are quickly visible and easy to debug.

Topics

The course topics will include both software design and implementation.

Design topics will focus on writing precise specifications, first for individual methods and then for classes (via abstract data types). Later, we will discuss ways to make certain kinds of bugs impossible, either with the type system or with design patterns, and ways to make our designs more extensible with subtyping and generics.

Implementation topics will focus on ensuring that code is fully correct for all inputs, which is achieved only through reasoning.

Textbook

There is no required textbook for the course. However, the lecture slides will at times make reference to the following book, which may be a useful reference for students working in Java:

• Effective Java by Joshua Bloch, 3rd Edition, Addison-Wesley, 2017.

Lectures

Lecture will be in person in SAV 260: lecture A meets MWF 12:30–1:20, and lecture B meets MWF 1:30–2:20. Lecture will be recorded and posted on Panopto, linked from Canvas. You are welcome to attend either lecture.

We have found that success in the course is correlated with in-person lecture attendance. Most lectures will have in-class activities involving discussion with peers and a brief written response on paper. These activities are graded on completion and count toward coursework (see Grading).

We recommend students take notes on paper. If you would like to take notes digitally, please use a tablet or other device that lays flat on the desk. If you must use a device whose screen is upright and visible to those behind you, then please sit in the back row.

Sections

Each week includes a TA-led quiz section. These will not be recorded, but the printed material used will be posted on this website.

Past experience indicates that section attendance is extremely important for being successful in the course. Similar to lecture, most sections will have in-class activities, graded on completion as part of coursework. If you cannot make your own section one week, you may attend another section that week instead; its activity counts the same.

Homework

Homework will be a mix of worksheet-style problems and programming problems.

Occasionally, we will discover errors in the problems after they are posted (typically, just small typos). We will maintain a log on the website with all changes made to the assignment. If (and only if) we believe there is potential for students to submit correct solutions to the earlier version, we will also announce the change on the message board. In particular, we will not announce it if the earlier version is obviously nonsensical. (Most typos fall into this category.)

Exams

The course will have a midterm exam and a final exam. See the Exam Mechanics section below for further details.

Getting Help

Students can ask questions at any time on the message board. They should receive a response within a day.

Each member of the course staff will have at least one office hour every week, where students can get one-on-one help. See the home page for times and locations.

Homework Mechanics

Submission

Homework will be turned in via Gradescope. After each assignment is reviewed by TAs, students will be able to see their feedback in Gradescope.

Regrades

If grading mistakes occur, students should bring them to the attention of the staff and see that they are corrected. They can do so by requesting a regrade in Gradescope, with the following caveats:

• We will only entertain regrade requests for one week after the grades are initially posted. In particular, students searching for extra points late in the quarter by requesting regrades of problems from early assignments will be ignored.

• While regrades can (and should) be used to fix mistakes where a correct solution is mistakenly marked incorrect, they cannot (and should not) be used to request changes to amounts of points deducted for errors. Deductions are applied consistently to all students. We will not give one student a smaller deduction than others who made the same (or very similar) mistakes, so do not ask us to do so.

Policies

Inclusiveness

Students should expect and demand to be treated with respect by their classmates and the course staff. All students belong here, and the staff is here to help them learn and enjoy a challenging course. If any incident occurs that challenges this commitment to a supportive and inclusive environment, students should let the staff know so the issue can be addressed. You can use the anonymous feedback tool if you would like to tell us something anonymously.

Late Policy

All assignments (homework, readings, and the project) are due Fridays at 11:59pm. That is a real deadline: it is when we expect your work to be submitted.

But we know things come up, and we want to accommodate different needs and unforeseen issues. The staff do not have the capacity to handle individual exceptions, so instead you can unlock one late day for each assignment, with no penalty, by starting early:

• Make progress and submit by Friday to unlock the late day. Then you may submit again through Saturday at 11:59pm.
• Each part needs its own Friday submission. Some assignments have separate parts on Gradescope, like HW1 Written and HW1 Coding. A Friday submission to one part does not unlock another. Within a part, a Friday submission still missing a file counts.
• Otherwise, there is no late day. If you do not make progress and submit by Friday, anything you submit after Friday scores zero.

You do not need an emergency to use the late day. If you finished on time, you can use it to polish or correct your work.

We expect a good-faith effort to get things in on time. Uploading the starter code, or making only minor changes to it, abuses this generous policy, and that is academic misconduct. See Academic Integrity.

For a serious emergency, email the instructors.

Collaboration

Students are allowed to discuss homework problems with others, but they must write or type their solution individually, without assistance.

Student solutions are not group work. Students can work together to solve problems, but then, they must leave the group, taking no written or electronic records of group work with them; wait at least 20 minutes; and then write up their solution on their own, unassisted.

This policy clearly precludes copying code or other solutions off of the Internet. Hence, students should not consult the Internet for problems or key-phrases. This includes Google, Stack Overflow, reddit, and any other website. However, students may consult the internet for ideas, definitions, and understanding general concepts.

Each student should keep anything they plan to submit as a solution completely private. Under no circumstances should they give a copy of their code or written work to another student or post it on the internet.

AI Assistance

This course follows the Allen School's shared AI policy template 4, AI for Support and Growth (Allen School version), with this course's authorized graded work filled in below.

Overview

You may use AI for general course questions and ungraded work. You may also use AI on graded work when that work is specifically listed in Authorized Graded Work. AI is not allowed on any graded assignment that is not included in the authorized list. Read the full policy carefully before using AI.

Definition of AI

For this policy, "AI" refers to generative AI systems that can produce or modify text, code, images, audio, video, or other content — whether in a single response to a prompt or by operating autonomously across multiple steps (e.g., chatbots such as ChatGPT, Claude, and Gemini; code assistants and agents such as GitHub Copilot, Cursor, and Claude Code; and AI-powered writing, translation, and image-generation tools). This includes AI features built into IDEs, operating systems, or browsers when they generate or substantially rewrite content, but excludes non-generative tools such as basic spell-check, grammar/syntax highlighting, and linters.

Rationale

AI can be valuable for exploring concepts, practicing skills, and developing one's capabilities. AI use is allowed on some assignments to give you experience working with these tools to supplement and grow your own knowledge. However, you are still ultimately responsible for your own learning, and some assignments will require you to demonstrate your knowledge and skills without the use of AI.

Authorized Graded Work

AI use is permitted only for the following graded work:

• Reading reflections, for light editing only: grammar, spelling, and trimming.
• Programming tasks whose handout explicitly authorizes AI. The handout states any limits, and for that task you may put its prompt and starter code into an AI tool.

AI use is not allowed on any graded assignment that is not listed above. The instructor may provide additional instructions or limits for any authorized activity.

Permitted Actions

• You may use AI for general questions and support–including concept explanations, study questions, practice problems, ungraded exercises, and exploratory coding–on all work.
• You may use AI to help develop your submissions only for the specific assignments listed in Authorized Graded Work, subject to any instructions or limits stated for that activity.

Prohibited Actions

• Do not use AI to produce work you submit on any graded assignment that is not listed under Authorized Graded Work.
• Do not use AI in ways that exceed the stated limits for an authorized graded activity.
• Do not submit AI-generated work, or lightly edited AI-generated work, as your own when the activity's instructions require work to be independently produced.
• If an assignment has stricter rules, those rules override this policy for that assignment.

Examples

• Allowed: using AI to explain a concept, generate practice questions, or help you study for a quiz.
• Allowed: using AI on an ungraded practice exercise or exploratory code that will not be submitted for credit.
• Allowed: using AI to help produce your submission on a graded assignment if that activity is listed under Authorized Graded Work.
• Not allowed: using AI to fix a bug or rewrite a proof for you on an assignment that is not listed under Authorized Graded Work.
• Not allowed: copying work produced by an AI into your submission that is not listed under Authorized Graded Work.

Required Attribution

If you utilized AI on an authorized assignment, specify the tool you used and how you utilized it in your work.

Example: "Used ChatGPT to produce an initial solution to all problems, which I then refined and corrected."

Guidelines for Effective AI Use

• Before using AI on graded work, confirm that the activity is listed under Authorized Graded Work and review any activity-specific instructions.
• Use AI to expand your thinking, not to replace it.
• Verify AI output carefully; AI can be inaccurate, incomplete, biased, or inconsistent with course expectations.
• Maintain control of your work: you should be able to explain and defend every part of what you submit.
• When in doubt, err on the side of not using AI and ask the course staff for clarification.

Data Privacy Expectations

• Do not upload course-specific materials—including assignment prompts, course code, datasets, class notes, hidden tests, solution keys, or other restricted content—to AI tools without the instructor's explicit approval.
• Some AI tools (e.g., Cursor, Claude Code, and similar coding agents) can automatically access files beyond what you explicitly share — including your entire local repository, file system, or connected accounts. Before using such a tool, confirm what it can access and restrict or scope that access so it does not read private repositories, hidden test files, other students' code, solution keys, or other restricted materials without your intent.
• Do not submit personal, sensitive, or university-protected information into AI tools.
• Be aware that prompts and outputs may be stored or reused by third-party AI providers.
• UW's Purple tool and certain GenAI tools provided by the Allen School for use in courses are designed specifically for UW students and staff and have stronger privacy protections than public commercial tools. When AI usage is permitted, using Purple or Allen School-provided tools is advised if possible.

Academic Integrity

Using AI in ways prohibited by this policy or by assignment-specific instructions will be considered academic misconduct and referred to CSSC. Students found responsible for violating this policy will, at minimum, receive no credit for the affected assignment. Stronger penalties, up to and including failing the course, may apply for repeated or egregious violations. In cases of group work, all students will be held equally responsible for violation unless there is clear evidence that a subset of team members were not involved or aware.

Accommodations and Approved Exceptions

Students with accessibility needs or other extenuating circumstances should contact the instructor to discuss exceptions. Accommodations or exceptions require instructor approval before they are used.

Academic Integrity

As noted above, the staff expects each student to write their own solutions, independently. Attempting to misrepresent another student's solution as their own would be unfair to the other students in the course and constitute academic misconduct in violation of the Allen School policy. Any such violation will be reported to University, and the instructors will make every attempt to ensure the harshest allowable penalty.

Note that, in cases where one student copies the solution of another, both students have violated the policy and both will be reported. Again, all students should keep their solutions private.

If a student is ever unclear about whether their discussions with other students went over the line, they should (a) ask and (b) describe their collaboration clearly on their assignment. If they do, the worst that will happen is losing some points. That is much better than the alternative.

Accommodations

Please refer to university policies regarding disability accommodations.

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW's policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Wellbeing

If you are struggling, please reach out to us or to these resources:

• The Allen School's mental health consultant, John Rumney, meets with Allen School students for free and in confidence, Monday through Friday, in person or online: same-day 15-minute drop-ins, or 30-minute appointments. Visits are brief, informal consultations, not a substitute for therapy, counseling, or psychiatric care. Schedule a visit.
• UW's Husky Mental Health Resources page lists the mental health resources on campus.

Grading

Course Grades

Your course grade combines two scores: coursework and exams.

Coursework score (C) is the weighted average of:

• 60%: homework and the project
• 20%: reading reflections
• 20%: in-class activities in lecture and section, graded on completion; we drop your 5 lowest

Exam score (E) is the weighted average of the midterm (40%) and the final (60%), or the final alone if that is higher.

Your course grade is the lower of your exam score and your coursework score, where coursework at 90% or above counts as 100%:

course grade = min(E, C / 0.9), where C / 0.9 is capped at 100%

In words:

• Your exams set your grade. Coursework cannot raise it above your exam score.
• Coursework can lower it: if C is below 90% of E, your grade is C / 0.9.
• Coursework past 90% earns nothing extra, so there is no reason to polish it further, with AI or otherwise. Spend that time learning the material.

For example:

• Exams 85%, coursework 95%: coursework counts as 100%, so the grade is 85%.
• Exams 85%, coursework 72%: coursework counts as 80% (72 / 0.9), so the grade is 80%.
• Midterm 60%, final 90%: the final alone (90%) beats the mix (78%), so E is 90%.

Exams show what you can do on your own, so they set the grade. Coursework is where you learn to do it, so you cannot skip it.

Exam Mechanics

There will be a midterm exam and a final exam.

The midterm exam will be held in lecture on Friday, November 13.

The final exam will be held on Tuesday, December 15, 12:30–2:20: lecture A in BAG 131, and lecture B in BAG 154.

If you have a conflict with either exam, please contact course staff as soon as possible and at least a week in advance of the exam.

Students must bring their UW ID and have it ready to be checked during the exam. You may bring one sheet of notes to each exam: letter size, handwritten, both sides. Otherwise the exams are closed notes, with no electronics.

The exams ask about the concepts the course teaches. The homework is designed to help you learn those concepts, so doing it carefully is good preparation.

Advice For Students

• Prefer the message board to office hours. Office hours get very busy, especially in the last 48 hours before assignments are due. (Yet another reason to start early is so that students can attend office hours that are less busy.) Since a message board answer can take up to a day, ask early.

• Do not skip class to work on homework, not even late in the quarter when students are more tired and busy. Doing so often seems like it will save time in the short run, but it will cost time in the long run.

• Think about which lecture material applies to that homework problem. Each assignment is intended to apply ideas from lecture, so if it does not seem that any lectures apply to a given problem, it is nearly certain that the student did not understand some part of some lecture.

• Do not blindly trust outside resources. Most people do not program like we do in this course. A student's goal should be to solve homework problems "like a 331 student" and not "like an average programmer". Outside resources may mislead you about what we are trying to teach you.

• Focus on understanding, not on getting points. A student's understanding of the material, not the points they received, is what will get them a job and ensure a successful career as a programmer.