The centerpiece of the course is a quarter-long project, done in groups of 2-4 students. The project should make a research contribution related to Software Engineering. As a research contribution, it should answer a question whose answer no one previously knew.
You may select (or modify) a project from among the list of instructors' suggestions, from your answers to Assignment 1, from other students' answers to Assignment 1, or from other sources. The instructors will help you choose, refine, and scope a project.
For instance, you might start from a programming problem that has vexed you and devise a solution (or demonstrate that it is a broader problem than previously recognized). You might attack one of the problems listed in a paper's future work section. You might take a promising paper that lacks experimental evaluation, or whose methodology is flawed, and perform a proper experimental evaluation.
You should also consider building on your strengths and finding ways to leverage other work that you are doing. For instance, if you do work in ML, you might consider how machine learning could be applied to problems of program comprehension or to filtering output from program analysis tools. You also might consider using a project that you are working on, or that you worked on in the past, as a test case for a tool. Synergies with your other work are welcomed!
Your final project report should be written in the style of a conference paper, and you will present it in a class “conference” at the end of the quarter. You are not required to submit your work to a conference or workshop, but you might choose to do so — this is the standard of work expected, and which the instructor will help you achieve. Each submission (see below) will build on the others (and incorporate all previous feedback), until your document is complete at the end of the quarter.
Please use the ACM proceedings style: https://www.acm.org/publications/proceedings-template (ACM Master article template for LaTeX). Do your work in a Git repository (or in Overleaf, which is accessible via Git). Give the course staff write permission to the repository.
You need not close the book on the area of research you choose, but you should do a good job of discovering and clearly communicating new knowledge in that area. Do not feel overwhelmed by the project or final report. The instructors will meet with you repeatedly for discussions, feedback, and assistance on all aspects of your project. For tips about writing a technical paper, you should read (among other resources) http://homes.cs.washington.edu/~mernst/advice/write-technical-paper.html.
Exact due dates are listed on the course website and on Canvas.
Select a group, select a project, and write a short (1-3 pages) project proposal that includes its thesis and technical approach.
Be sure to motivate your project. Your paper should start by stating the problem. It also should give a use case — essentially, a story about how the approach is used or what to learn from the proposed evaluation. For example, a software developer wants to perform a particular, realistic task, but cannot. Be specific about the problem and about why current techniques do not address it. Your approach solves that problem. An architectural diagram of how the pieces of your system fit together may be a useful summary.
Include your evaluation methodology. The philosopher of science Karl Popper states that a theory is scientific only if it is falsifiable — if some experiment exists that would disprove it. This section should include the research questions (e.g., an experimentally refutable thesis or hypothesis) and how you will answer them. You should also state your evaluation metric, which should be, at least in part, quantifiable. Another way to think of this is: how will you convince a skeptical reader that you succeeded? They aren't going to just take your word that your project was a success.
Clearly state the scientific question or contribution. For example, how is your project an (incremental) advance over previous work? Ideally, your results will be transferable to other domains: they will change the way that others think or act in the future, or provide guidance to programmers or to tool developers or both. Oftentimes, much of your project is not novel. That's OK — engineering is necessary in order to do experiments or to build on previous work — so long as you can point out the part that is novel. A new way of combining previously-known techniques is sufficiently novel.
Meet with the instructors for feedback on your project proposal. You should also solidify the technical approach.
Submit a version of your report that includes the introduction, description of the technical approach (e.g., algorithmic details), related work, and experimental methodology (i.e., evaluation plan). These sections should address the feedback you have received so far.
It is okay if this description changes as you discover flaws in your initial ideas about the methodology, subject programs, and so forth. However, it is not acceptable to simply skip this assignment by submitting something that is vague or incomplete. The purpose of this milestone is to make you think about these issues and to enunciate them in time to get feedback from your teammates, from yourself when you see what you have written, and from the instructor. For any scientific project, working hard on this step will save you a lot of time in the long run.
Your experimental methodology section should not contain vague descriptions or missing aspects of the methodology. It should specify subject programs, experimental protocols, specific tasks for any user evaluation, specific metrics and how they are computed, etc. The paper should contain enough details that a reader could complete your project without making significant design decisions.
The evaluation plan should also justify your choices by explaining why your chosen metrics are the right ones, and what insights they reveal about your work. (For example, do they address the novel part of your technique, or are they end-to-end measures? Can they be directly compared to previous work, or not?) It should also indicate your criteria for success. (You should lay those out now, so that you have an objective measure when the research is complete.) This version of the paper should include all the tables or graphs (if any) that will appear in the final paper, and the structure of any textual sections of the evaluation. The tables or graphs will be empty or contain mock data for now, but you should know what they are and how they will be populated eventually.
You should have already divided the work among members of your group.
You should be able to reuse most of the text from your proposal, but you will also have to augment it. Each revision of the paper should address the feedback from the instructors.
Submit a version of the report that includes partial results. This draft should also include a description of the interesting parts your implementation — enough information to enable a reader to re-implement it in detail.
If you are building a tool, you should have a prototype implementation that can run correctly end-to-end on a small example program. (For example, a compiler might be able to compile the factorial program.) Having a working implementation now will enable you to enhance it and to run experiments during the next few weeks.
As a rule of thumb, by this point you should have one row or column in each table filled in. Or, if you are doing a case study or qualitative experiment, then you should have completed a pilot study. You will likely find that, besides these results and the implementation description, your paper did not change much in this submission.
You should plan to talk for 15-20 minutes, followed by 5-10 minutes for questions (though the questions may come during rather than after the talk). As with any talk, be sure to practice it before you present in class. Be sure to include plenty of examples! (Even after receiving this advice, most groups fail to include enough to make their ideas comprehensible.) For more tips, see http://homes.cs.washington.edu/~mernst/advice/giving-talk.html.