Lectures and Details

Meetings

Lecture — MW 4:30-6pm 1670 BBB, starting January 3rd

Discussion 1 — F 9:30-10:30am 1017 DOW, starting January 12th (led by Yu)

Discussion 2 — F 10:30-11:30am 1018 DOW, starting January 12th (led by Yu)

Discussion 3 — F 1:30-2:30pm 1014 DOW, starting January 12th (led by James)

Textbook

None (but see below)

Course Staff

Yu Huang

James Perretta

Madeline Endres

Office Hours

M — 9:00am - 11:30am — 2909 BBB, Yu

M — 1:30pm - 2:00pm — 4636 BBB, Wes (any topic)

T — 12:30pm - 2:30pm — Duderstadt basement, James

W — 11:30am - 1:00pm — 2909 BBB, Yu

W — 1:00pm - 2:0pm — Duderstadt basement, James

W — 2:30pm - 3:00pm — 4636 BBB, Wes (any topic)

W — 6:00pm - 7:00pm — 2909 BBB, Yu

R — 11:30am - 1:30pm — Duderstadt basement, Madeline

F — 11:30am - 2:30pm — Duderstadt basement, Madeline

Overview

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?

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 at least 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 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, and Claire Le Goues.

Readings

One of the most common student complaints is about buying textbooks that are both expensive and out of date. As a result, I have structured this course so that there is no textbook and all of the readings are available on-line for free.

Because software engineering is often more engineering than science, the basic concepts are often 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). These detailed readings serve to flesh out high-level concepts that can otherwise be introduced via Wikipedia without costing you money.

The flip side of this is that you should actually do the reading. It will help you both in terms of understanding the lectures and also in terms of completing the assignments.

Finally, you may notice that some of the readings are marked optional. Next to each such optional reading I have included a small "advertisement" for why you might want to take a look. The optional readings are not required for any class assignments, but there may be extra credit questions on exams that reference them.

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.

Grading Examinations

There will be two in-class written examinations for this course. No final examination is currently planned. Course grading will center on homework assignments and examinations. The approximate grading breakdown is as follows:

  • 10% Homework 1 — Test Coverage
  • 7% Homework 2 — Test Automation
  • 10% Homework 3 — Mutation Testing
  • 7% Homework 4 — Defect Detection
  • 10% Homework 5 — Debugging Automation
  • 15% Homework 5 — Contribution
  • 5% Reading Quizzes
  • 18% Examination 1
  • 18% Examination 2

Regrade and Makeup 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 the course forum (privately). 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.

If you miss an assignment deadline or in-class activity, we can be very lenient about extensions or makeups but only if you provide documentation. For example, for death or bereavement, a copy of the obituary or funeral program suffices; for illness or injury, any sort of doctor's note suffices. This policy follows that of other professors in the department.

If you miss an assignment deadline without an approved, documented extension, you may receive no points. for that assignment. In some extenuating cases you may receive h% off, where h is the number of hours late.

The dates for the in-class examinations are posted well in advance. To request a rescheduled examination (e.g., for reasons other than a documented one as above, etc.), you must notify us via the course forum (privately) at least one week before the date of the examination. Note that requests need not be granted.

All course materials submitted for a grade must be turned in by midnight on the last date listed on the course syllabus (Monday, April 16th).

Research

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.