CS373: Software Engineering



Course Information

  • Instructional Mode: Face-to-face
  • Help Sessions: M 6-8 pm, W 6-8 pm, GDC basement

Instructor

Prof. Glenn Downing
https://www.cs.utexas.edu/~downing/
Office Hours: MF, 12 pm, GDC 6.304

UGCAs

Aishwarya Balaji
GitLab: https://gitlab.com/aishwaryabalaji/
Office Hours: T 11 am, GDC basement
Justin Ye
GitLab: https://gitlab.com/justinye1/
Office Hours: T 2:30 pm, GDC basement
Praveen Mogan
GitLab: https://gitlab.com/pmogan77/
Office Hours: W 5 pm, GDC basement

Quan Truong
GitLab: https://gitlab.com/quanxtruong/
Office Hours: M 5 pm, GDC basement
Trina Goetz
GitLab: https://gitlab.com/trinagoetz/
Office Hours: M 2 pm, GDC basement

Welcome Message

  • Sign into Canvas and confirm your registration for the course. Check your grades regularly.
  • Confirm that you can access Ed Discussion on Canvas to post class questions.
  • If it involves something personal, like your grade, please make a private post on Ed Discussion.
  • Sign up for a GitLab account and clone the class repo to access the examples, exercises, and notes.
    • git clone https://gitlab.com/gpdowning-ut/cs373.git
  • Sign up for a HackerRank account for the first project.
  • Sign up for a Zoom account. You must use your UT EID credentials. Ensure that you have the latest version of the app: 6.5.3

Communication

  • Please use Ed Discussion for all of your communication with the staff.

Catalog Description

  • Introduction to current knowledge; techniques; and theories in large software system design and development.

Overview of the Class

Week Date Topic Problem Project
1 25 Aug syllabus, docker 1
2 01 Sep assertions, unit tests, coverage 2
3 08 Sep exceptions, iteration, recursion, reduce() 1
4 15 Sep unpacking, operators 3
5 22 Sep iteration, range, types, yield 4
6 29 Sep comprehensions, closures, decorators 2
7 06 Oct functions, regulalar expressions, context managers 5
8 13 Oct relational algebra, select, project, cross join, theta join, natural join 6
9 20 Oct SQL, show databases, show engines, create tables, insert, select, subqueries, join 3
10 27 Oct joins, Java 7
11 03 Nov inner classes, singleton, observers, abstract methods, abstract classes 8
12 10 Nov strategy, reflection, factory 4
13 17 Nov factory method, abstract factory 9
14 24 Nov Fall Break
15 01 Dec presentations 5
16 08 Dec presentations

Lectures Online

  • This class is using the Lectures Online recording system. This system records the audio and video material presented in class for you to review after class. Links for the recordings will appear in the Lectures Online tab on the Canvas page for this class. You will find this tab along the left side of the navigation in Canvas.
  • To review a recording, click on the Lectures Online navigation tab and follow the instructions on the page. You can learn more about how to use the Lectures Online system at http://sites.la.utexas.edu/lecturesonline/students/how-to-access-recordings/.
  • You can find additional information about Lectures Online at https://sites.la.utexas.edu/lecturesonline/.

Pre-Requisites of the Course

  • Prerequisite: Computer Science 429 or 429H with a grade of at least C-.

Software

Platforms
  1. AWS
  2. Canvas
  3. Docker
  4. Ed Discussion
  5. Ed Lessons
  6. GitLab
  7. Grammarly
  8. HackerRank
  9. Kattis
  10. Perusall
  11. Slack
Tools
  1. Black
  2. Bootstrap
  3. CheckTestData
  4. Coverage
  5. D3
  6. DB Diagram
  7. Flask
  8. GCov
  9. Jest
  10. Make
  11. Mocha
  12. MySQL
  13. Namecheap
  14. PechaKucha
  15. PlantUML
  16. PostgreSQL
  17. Postman
  18. PyDoc
  19. PyLint
  20. PyTest
  21. React
  22. Recharts
  23. Selenium
  24. SQLAlchemy
  25. UnitTest

Learning Outcomes

Altitude
  • appreciate the value of automatic builds
  • appreciate the value of collaboration
  • appreciate the value of component reuse
  • appreciate the value of continuous integration
  • appreciate the value of refactoring
  • appreciate the value of software testing
Knowledge
  • algorithms
  • assertions
  • class vs. instance data
  • class vs. instance methods
  • classes
  • containers
  • design patterns
  • exceptions
  • iterable vs. indexable
  • iterators
  • makefiles
  • UML
  • unit tests
Skill
  • algorithm design that requires the weakest capabilities
  • container design that provides the strongest capabilities
  • continuous integration design
  • decorator design
  • lazy-evaluation design
  • object-oriented design that minimizes setters and getters
  • makefile design
  • software coverage
  • software documentation
  • software formatting
  • software refactoring
  • software testing

Grading Policy

  • We expect each student to abide by the University of Texas Honor Code: "As a student of The University of Texas at Austin, I shall abide by the university's core values and uphold academic integrity." We permit discussing problem solutions with other students, asking others for conceptual help with a problem, looking online for tutorials explaining how to solve a problem, or getting help from a classmate debugging code you wrote. However, all code you turn in must be your own. The penalty for copying code (either from another student or the Internet) is an F in the course.
  • We will compute your grade with an innovative approach called specifications grading.
  • We will evaluate each assignment to see whether it meets the specifications.
  • Here's the EMRN scale:

  • The Canvas grade book uses a numerical scoring system and therefore we will map the EMRN scale onto a numerical scale:
    • E (3) [Excellent/Exemplary]
    • M (2) [Meets Expectations]
    • R (1) [Revision Needed]
    • N (0) [Not Assessable]

Assignments

Assignment N (0) R (1) M (2) E (3)
17 exercises
  • auto-graded by Ed Lessons
  • exercises are collaborative
  • makeups within one day
  • only two times
  • 2 out of 3 points
  • 3 out of 3 points
  • two E (3)s make up one R (1)
14 papers
  • auto-graded by Perusall
  • papers are collaborative
  • lates within one day
  • only two times
  • 2 out of 3 points
  • 3 out of 3 points
  • two E (3)s make up one R (1)
9 problems
  • auto-graded by Kattis
  • problems are individual
  • lates within one day
  • only two times
  • 2 out of 2 points
  • no E (3)s
5 projects
  • auto-graded by HackerRank
  • first project is individual
  • remaining projects
    • in groups of five
  • lates within one day
  • only two times
  • resubmissions within one week
  • only two times
  • 2 out of 2 points
  • no E (3)s
41 quizzes
  • auto-graded by Canvas
  • quizzes are collaborative
  • makeups within one day
  • only three times
  • 2 out of 3 points
  • 3 out of 3 points
  • two E (3)s make up one R (1)
  • For N (0)s or R (1)s over the last three weeks, makeups require a documented emergency excuse, medical excuse or UT-related event.
  • You must submit an emergency excuse to Student Emergency Services (SES). If the excuse is approved, SES will contact the instructor.

Late Add

The last day to add is the fourth class day, Thu, 28 Aug. If you add on that day, you will have already missed one problem and one quiz. You can make up both, but they will count against your two makeups.


Grades

Letter Percent Exercises Papers Problems Projects Quizzes
17 14 9 5 41
A 94% 16 13 8 5 39
A- 90% 15 13 8 5 37
B+ 87% 15 12 8 4 36
B 84% 14 12 8 4 34
B- 80% 14 11 7 4 33
C+ 77% 13 11 7 4 32
C 74% 13 10 7 4 30
C- 70% 12 10 6 4 29
D+ 67% 11 9 6 3 27
D 64% 11 9 6 3 26
D- 60% 10 8 5 3 25
  • Your course grade will depend on the number of assignments you complete with an M (2) or an E (3). It will then be the lowest grade in all assignment categories.

Generative Artificial Intelligence

  • AI tools (such as UT's Microsoft Copilot) and similar assistants can be valuable resources for learning and improving your code.
  • However, in this course, you are expected to use AI as a helper, not as a coder.
    • Do use AI to ask questions about concepts, debugging strategies, error messages, or ways to improve the clarity, style, or efficiency of your code.
    • Do use AI to deepen your understanding of why your code works, or doesn’t, and to learn alternative approaches.
    • Do not ask AI to generate solutions, write complete programs, or provide code that you then submit as your own.
  • The goal is for you to learn to think and code independently.
  • There is nothing to lose and everything to gain by asking an AI not to do your work but to critique it instead!

Overview of all Major Course Requirements and Assignments


MOSS

  • We will compare your programming assignments with Kattis and MOSS.
  • You may share design ideas with your fellow students. You may not share code in any way.

Canvas Assignments


Required Course Materials

  • No required materials.

Recommended Course Materials


Final Exam Date and Time

  • No final exam.

Class Canvas Site


Notice of Academic Accommodations from Disability and Access (D & A)

  • The university is committed to creating an accessible and inclusive learning environment consistent with university policy and federal and state law. Please let us know if you experience any barriers to learning so I can work with you to ensure you have equal opportunity to participate fully in this course. If you are a student with a disability or think you may have a disability and need accommodations, please get in touch with Disability and Access (D & A). If you have already registered with D & A, please deliver your Accommodation Letter to me as early as possible in the semester so we can discuss your approved accommodations and needs in this course.

University Policies and Resources for Students Canvas Page

  • This Canvas page is a supplement to all UT syllabi and contains University policies and resources that you can refer to as you engage with and navigate your courses and the university.

Copyright © Glenn P. Downing, 2008-2025
Updated 27 Aug 2025