COURSE OVERVIEW
This course introduces students to LLM-based approaches for problem solving in software engineering. Students will learn from the state of the art research and practice about how to frame coding problems effectively, generate and refine solutions using AI tools, implement and test code collaboratively with AI, review code and assess software quality, security, and performance. Through both reading research papers pushing the boundaries in the use of LLMs in software engineering and practical advice from grey literature like practitioner blogs, students will gain hands-on practice in leveraging AI as a coding partner while developing critical thinking and problem-solving skills.

COURSE SCHEDULE


Classes starts Jan 6 2026.
No office hours - Email me when you have a question.
Contact Instructor: Mei Nagappan . Please prefix the subject line of your email with the code [CS846] for a timely reply.

COURSE OBJECTIVES
We have a few main objectives in this course:

• Be able to read and understand a research paper/practitioner blog about the use of LLMs in SE.
• Identify the guidelines/best practices pointed out in the paper
• Collaboratively come up with a set of code examples that the rest of the class can experiment on to understand the guidelines.
• Experiment with the guidelines to see where it fails.
• Documenting the guidlines and the failures.

AI Use
We will use Github CoPilot in the class. As a student you have a free licence to it. Since this is a class about coding with AI, you are allowed to use AI tools anywhere unless noted otherwise. However, everyone is required to acknowledge how and where they used which AI tools in all reports and presentations. Failure to acknowledge properly will result in academic integrity violations.

COURSE DELIVERY and GRADING
The class is held in DC 2585 every Tuesday at 11.30 AM - 02.30 PM EST.

The weekly topics and readings

1. A detailed schedule is available here.
2. 30% - Student Presentations and Guidelines due Every Friday (before the corresponding Tuesday) by 5.00 PM: Each week 2-3 students will be assigned to the readings of that week. They will create a presentation that distills the guidelines, provide the reasoning for the guidelines, any necessary context, and limitations from the readings on that topic (15%). They will also upload a markdown file with a set of examples, each of which has a task description and all associated code, that the rest of the class can experiment with (15%). Both of these will be uploaded to the appropriate dropbox in Learn.
3. 18% (2% for each of the 9 weeks that you are not presenting) - Discussions During Class:
   1. The rest of the class will be provided with the task descriptions and code examples first. They will carry out the task with GitHub Copilot in whatever way they so choose. Each student will document how go about solving the task.
   2. The students responsible for that week will then present the guidelines.
   3. The rest of the class will try out the tasks again with the guidelines this time and record what was different and if the guidlines helped and in what way.
   4. We will discuss the findings in class.
4. 27% (3% for each of the 9 weeks that you are not presenting) - Student Feedback due Every Friday (after the corresponding Tuesday) by 5.00 PM: The students will submit what they documented in class in a specified format.
5. 25% - At the end of the term: The students who presented the guidelines will collect all the feedback from the students, collate them and create a report (15%) in a specified format and a presentation in a specified format (10%).
6. Sharing the reports: The instructor team will collate all the reports as one document and edit it before making it available on the course website under a GPL licence. This will be done in the weeks following the end of the term. Each student will be given the option to include/not include their names as the author to the respective sections.

COURSE REQUIREMENTS
Students are expected to have background in coding, software development and software engineering.

Administrative Notes

Land Acknowledgement

Mental Health Support

The Faculty of Math encourages students to seek out mental health support if needed. On-campus Resources:

• Campus Wellness
• Counselling Services: counselling.services@uwaterloo.ca/ 519-888-4567 ext 32655
• MATES: one-to-one peer support program offered by Waterloo Undergraduate Student
• Association (WUSA) and Counselling Services: mates@wusa.ca
• Health Services: located across the creek from the Student Life Centre, 519-888-4096.

• Good2Talk (24/7): Free confidential helpline for post-secondary students. Phone: 1-866-925-5454 (Ontario and Nova Scotia only)
• Here 24/7: Mental Health and Crisis Service Team. Phone: 1-844-437-3247 (Waterloo Region only)
• OK2BME: Set of support services for lesbian, gay, bisexual, transgender or questioning teens. Phone: 519-884-0000 extension 213 (Waterloo Region only)
• EMPOWER ME 1-833-628-5589 for Cdn./USA other countries see: http://studentcare.ca/rte/en/IHaveAPlan_WUSA_EmpowerMe_EmpowerMe
• EMPOWER ME in China: China North 108007142831; China South 108001402851

• We will gladly honour your request to address you by an alternate/preferred name or gender pronoun. Please advise us of this preference early in the term so we may make appropriate changes to our records.
• We will honour your religious holidays and celebrations. Please inform of us these at the start of the course.
• We will follow AccessAbility Services guidelines and protocols on how to best support students with different learning needs.

Academic Integrity

• In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. [Check the Office of Academic Integrity for more information.]

Grievance

• A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt, please be certain to contact the department's administrative assistant who will provide further assistance. , Student Petitions and Grievances, Section 4.

Discipline

• A student is expected to know what constitutes academic integrity to avoid committing an academic offence, and to take responsibility for his/her actions. [Check the Office of Academic Integrity for more information.] A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about "rules" for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate associate dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties check Guidelines for the Assessment of Penalties.

Appeals

• A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground.
• A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals).

Note for students with disabilities

• AccessAbility Services, located in Needles Hall, Room 1401, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with AccessAbility Services at the beginning of each academic term.