Mirvish/TicketKing Lies About Security

Not cool in this geek’s books.

“Relevant After” for todo lists

Andrew and I used FogBugz to organize ourselves leading up to our YC interviews last fall. I just filled out a customer survey; here’s what I told them for “Missing Features”:

I’ve wanted this from every project-management system I’ve ever used: A “Relevant after” field that hides items from all default views until a trigger (e.g. date, or other task completion).

This would let me brainstorm todo items without being annoyed by clutter that isn’t relevant yet.

For example, I handed in a draft of my Master’s thesis on Monday afternoon. My second reader told me, “OK, I’ll read it Wednesday morning and get back to you.”

As of that moment, I have a new todo item: Either deal with her feedback or remind her that I need her feedback. This new task is not relevant to me until Wednesday except when planning other tasks to be done that day.  And I know how you feel about software that gets in your face with irrelevant shit.

English is hard

I asked my sister and father for help on a point of grammar. Each found a separate flaw in my example sentence.

Where does an adult go to improve his writing?

The Roles of Journals

Academic journal publications are the lifeblood of science: The medium for serious communication, the marker of career success, and moderator of scientific integrity. Modern technology is disrupting the funding model that has supported the system for hundreds of years. As things change, it becomes worthwhile to consider the various functions journals perform. Only with new understanding can we identify the pieces we shall need to rebuild if the established structures cannot adapt.

Before the Internet, printing and distributing physical documents was sufficiently complicated to warrant specialized organizations. It was natural to capture the value of this service by asking readers to pay. These subscriptions effectively subsidized other journal services.

Now, printing and distribution are cheap commodities. For pennies, a scientist can make work available to every interested reader. This undermines the subscription business model, and it isn’t clear how existing journals can measure or capture the value of their other jobs.

To stave off collapse, journal publishers now fight bitterly to keep their publications closed to all but paying subscribers. This pits them directly against their contributing authors, who favour openness on principle and on the pragmatic realization that open publication means more citation.

Let us explore some of the unfunded roles journals play:

Editing: Journal-funded editors raise the basic quality of writing in scientific publication, filtering out spelling and grammatical errors.

Coordination of peer review: Journals arrange for qualified scientists to review others’ work, guaranteeing authors a source of feedback that is sometimes even useful.

Formalization of discussion: Journals provide a generally-accepted forum for comments on a paper and a system of retraction for severe flaws discovered after publication.

Reputation assignment: Within every scientific subfield, there is a generally-understood status hierarchy of journals. Publication in prestigious journals bestows honor on a paper, its authors, and their institutions. In some fields and journals, the order of the author list tweaks this assignment of credit.

Some fields formalize this system into a mathematical formula in an attempt to fairly measure the impact of a scientist, institution or journal.

Attention assignment: Journals regularly aggregate related content in a field, focusing the attention of interested parties. The structure of journal articles permits sophisticated searches of the literature.

Durability assurance: Journal publications are archived reliably, making it rare for a piece of work to become completely inaccessible.

Canonical citation: The scientific community generally agrees on how to cite articles from journals. A journal article citation is sufficient for a reader to uniquely identify the work being discussed.

Any serious effort to replace or fix journals will need to consider more than just the obvious, formal processes. The unwritten social roles and subtle benefits must not be overlooked.

The New Airport Security

Rory and Aran fly to San Jose.

We pack checked baggage only, knowing we won’t be allowed to carry much on.

The airport is empty. Flying on Wednesday afternoon has its perks.

It takes a while to figure out that flying NorthWest Airlines means checking in at Delta.

We breeze through checkin. I had a cheerful, friendly agent who helped me through checkin and waived my checked baggage fee of $20. Rory was not so lucky, with a surly woman who gave no such favour.

Customs had a couple agents working and no line. My agent found out I was interviewing at Yahoo!, so he asked me why they should hire me instead of some other candidate.

The usual X-Ray screening station was overstaffed. We didn’t wait, and the agents with nothing to do were joking around.

Beyond usual security, there was a new station staffed with a dozen RCMP men and women. They were doing full pat-down searches and inspection of electronics. They were way more cheerful than the usual security types. I guess they still had their souls. We cracked jokes about my trying to earn a pat down from one of the cute female officers. (No luck.)

Total time from entering the airport to reaching my gate: Less than one hour.

Language Monoliths

“Write programs that do one thing and do it well.”

The Little Computer Scientist appreciates simple design. To design systems, design teams of cooperating specialists. To keep warm, wear a coat on your body, scarf on your neck, boots on your feet, and mittens on your hands. Don’t wear a seamless fullbody wintersuit—it will be hard to go pee.

Computer people mostly get it. We write special-purpose programs and break them down into special purpose pieces. We try not to make mittens that spontaneously unravel when you change your hat.

We get it, that is, until we build our programming languages.

Programmers have gotten used to the idea that choosing a programming language is a big important decision. Why? Because you can’t just choose a syntax expressing a few core abstractions. For each language, only a handful of editors are reasonable. There’s a standard library to mentally index. There’s an incumbent community of developers, and they have a philosophy of software development. There are idioms to learn and implicit “you’re a bad programmer unless you do it this way” rules. There’s a generally accepted tao of quality assurance. There’s a small council of key thought leaders and They have RSS feeds. There’s a preferred build tool, a deployment tool, and the non-standard-library standard library, like CPAN or Ruby Gems or PyPI.

Now, there are many valid forces that push things to be this way. The people who go with the grain aren’t dumb.  But we should recognize the problem and ensuing opportunities here. Monoliths are bad. Programming languages are intrinsically monolithic. Working to oppose monolithic forces is good.

Many projects do it right. XML, JSON, Protocol Buffers and Avro tackle interlanguage data representation: programmers should use them for making their data objects. Tools like CouchDB with language-agnostic HTTP interfaces are winners. CoffeeScript puts a new syntax on JavaScript: A worthy experiment. Scala and Closure compile to the JVM: There’s little excuse for language designers building VMs any more.

The Little Computer Scientist hopes to see more work breaking languages down. Make them smaller, less monolithic, more flexible. Compose them of special-purpose pieces. Expose the AST as a protobuf. Give a meta-circular interpreter and little compiler so that building correct new implementations is a day’s work. Isolate the syntax as much as possible so it’s easy to make correct highlighting editors, pretty printers and style-checkers.

Decouple, specialize, cooperate. One thing done well is a good thing.

The Death of JavaScript

Jash Kenas released CoffeeScript today, a programming language that compiles to human-readable JavaScript.

It addresses a real need, exemplified by this image:

JavaScript was designed quickly and extended by dumb committees. It’s a messy language with a potent core. CoffeeScript aims give programmers the core without the mess. It’s nice. It’s broken in many of the same ways as JavaScript, because it’s just syntactic sugar on JavaScript, and JavaScript’s problems run deeper than syntax. But it can grow to fix these.

CoffeeScript and languages like it are the reason why JavaScript is going to die. Steve Yegge thinks JavaScript is the next big language, poised to take over the programming world. I disagree.

CoffeeScript is a baby in the growing family of languages that compile to JavaScript. These include Scheme, Hop, Java (via GWT), Smalltalk (via Clamato) and Objective-J. These languages are good. A programming language is a programmer’s main tool—and is thus the most rewarding place for improvement.

Imagine you can quantify the productivity of programmer using JavaScript. Over time, everything’s getting better: Libraries, JavaScript VMs, tools like Google Closure. JavaScript programmers are getting better at writing more better faster code as time goes on. Hurray!

There is another productivity curve. This is the curve of compiles-to-JavaScript language users. In this world, you can increase your productivity by using better languages.

Here’s the question: Does the compiles-to-JavaScript curve intersect the raw JavaScript curve anytime soon? If not, Steve Yegge is right. JavaScript wins as long as web browsers are the dominant application platform. But if it does… if it has happened already, even… then JavaScript as a human-use language will die, relegated to being a target language for compilers, like assembly code.

Here’s a brainstorm of some implications.

• The ECMAScript committee should stop making up features to make JavaScript easier on programmers, and start changing features to make JavaScript more of a good target language.
• The V8, SpiderMonkey, Carakan and other JavaScript implementations should start benchmarking on workloads that come out of code generators. (Actually, V8 already does this. They should do it more.)
• The folks working on server-side JavaScript frameworks should just stop and go lie down, for the love of god.
• Finally, future HTML standards should make allowances for JavaScript to be replaced. Something along the lines of <script language="CoffeeScript" compiler="coffee-script.js">code</script> could mean: Run code if this browser understands CoffeeScript natively. Otherwise, load coffee-script.js, find the compile function inside, call compile(code), and eval the result as JavaScript. I say this not as a proposal of how the forward-compatibility should work (I can think of at least one other way) but to make the point that we should be talking about it.

Research Ideas

I’m planning Project February: What Aran Does When His Master’s Is Done. I’ve got a startup on standby, and interviews with companies large and small.

One possibility to stay in academia for a PhD. If I were to do this, I would need a research direction. This would then influence who I’d court as a supervisor (Greg is leaving for industry) and therefore where I’d go to school.

I could stay at the University of Toronto (I’m already in. I love it here. Path of least resistance.) or look elsewhere and aim for a later start date. Washington is on the up and up. Stanford is the de facto Silicon Valley university. It would be a good fit for my entrepreneurial ambitions. And I follow the work of researchers at many other UK and American universities.

Of course, there’s no guarantee I could get in to these places. I have no connections, no reputation, no GRE scores and currently no publication record.

In any case, the first step is to figure out if I want to do more research. Since I’m brainstorming anyway, I figure I’d post my thoughts.

Dataflow languages

Dataflow languages don’t just separate what from how, as declarative languages do, they separate what and how from when. Spreadsheets are dataflow, and there are a couple dataflow languages and libraries, but I don’t feel dataflow programming as a general-purpose paradigm has gotten the attention it deserves. Dataflow becomes especially interesting when the execution is distributed and operations are expensive, since there are all sorts of wonderful scheduling problems and optimization opportunities.

Performance Types

Traditional separation of interface from implementation deals only with functionality. But sometimes, we want to specify performance characteristics in an interface. Take real-time programming or large-scale programming for example. What’s a good language for describing performance specifications? Can we encode it into a type system?

A/B User Interfaces

Modern web user interfaces are optimized on-the-fly. Can we make better tools for UI designers to manage hundreds of variations of a UI?

Algorithmic Information Theory and Machine Learning

Algorithmic Information Theory gives us a quantitative way of thinking about intelligence. Can we learn new things by bringing this view to bear on machine learning? Example research project: Define a programming language with boolean strings as values, do learning by highly-optimized program generation.

ESE

I find it easy to come up with ideas in Empirical Software Engineering, so there’s a disproportionate number of these below.

Tool-aware development

We’re all familiar with software quality attributes like performance, readability, security and so on. And we’re aware of good coding practices like mnemonic variable names and short functions. There’s a new quality attribute in town, though, and as far as I know, nobody’s talking about it yet.

Modern software engineers use a variety of tools, such as version control systems. In the presence of these tools, certain constructs become worse.

For example, in C, you might see a construct like

#define some_flag_1 0x0001
#define some_flag_2 0x0002
#define some_flag_3 0x0004


Nothing wrong with this on the surface, but in the presence of concurrent development, version control and automerges, this code is a hotspot for a merge error if two programmers add flags.

As another example,

y = f(x);
if(y < z)


is more friendly to someone stepping through a debugger than

if(f(x) < z)


because most debuggers only let you step through statements, not expressions.

One last example: On some teams, everyone uses the same tools. On others, everyone uses different tools. Some coding practices are annoying to deal with in certain development environments. Homogeneous tool environments call for different practices than heterogeneous environments. I believe research is warranted into the ways our coding practices should change based on the development context.

Tool-aware languages

How do you design a programming language differently when you know the developer won’t just have a text editor, but will have a debugger, static checker, syntax highlighter, unit test suite, doctests, version control system, code reviews and more?

Software Development Practice Interactions

Nobody uses code reviews or unit tests or pair programming or whatever in isolation. And not all bugs are equally important. Which practices find which bugs? And how do they overlap?

Empirical Bug Prioritization

Modern software is shipped rife with known bugs. Project managers prioritize bugs and optimize for “good-enough” quality in a largely ad-hoc way. Can we offer something better than “estimated number of affected users * expected annoyance” for prioritization? Are there unpublished best practices to be discovered and optimized?

Technique Drills for Programmers

Technique drills are used in just about every other skilled human activity. Why not programming?

Bug introduction in detail

How far can we push Ko’s framework on error introduction? Can we do a better job of putting programmers under the microscope and really zoom in on what happens when bugs are introduced?

Syntax Pragmatics

I’m pretty sure from my work on DSLs that syntax matters. Can I drop another level of abstraction and find out why? This would be an investigation of the connections between semiotics, programming languages and information visualization.

A Language for Empirical Programming Language Design

If we want to learn about the usability of programming language features, we’d like to be able to turn features on and off. Most languages are designed to be as good as possible for real-world use. What about a language with dozens of syntactic and semantic knobs just for experimenting on features in isolation?

What do you want?

I was asked today what I wanted in a job.

It was surprisingly difficult to answer.

I like achievable challenges. That’s obvious. But—getting some CSS to display just right on Internet Explorer 6 is an achievable challenge, but it’s not work I’ll enjoy. Crushing a hard bug is fun, but debugging code that should never have made it through review is not. Writing efficient code is fun. Dealing with low-level memory management, not-so-much. Type errors in loosely typed languages feel preventable. Not fun. Designing a good API: Fun. Dealing with legacy constraints: Not fun.

These days it’s popular to filter software engineers: Front-end or back-end? Applications or systems?

But this isn’t a key distinction for me. It doesn’t capture the essence of what I care about. I could be happy working on front-end or back-end code. I could be unhappy working on front-end or back-end code.

Now, moments after my interview ended, I’ve managed to figure out the answer…

Accidental complexity: Not fun. Intrinsic complexity: Fun.

On Mobile Phones and Driving

Worth repeating, from Jakob Nielsen’s excellent newsletter:

In an opinion poll, the New York Times asked whether Americans wanted to outlaw mobile phone use while driving:

> http://www.nytimes.com/2009/11/02/technology/02textingside.html

80% of respondents said that using a HAND-HELD cellphone while driving should be illegal.

But almost 90% said that it should be legal to talk on a HANDS-FREE cellphone.

Completely OPPOSITE INTUITIONS about the danger of these two ways of using a mobile phone. This despite the fact that all studies show that it’s EQUALLY DANGEROUS to use hands-free and hand-held mobile phones while driving.

Research has found that the danger comes from the cognitive distraction of carrying out a conversation with somebody who’s not in the car. The problem is not holding the phone with one hand while driving with the other. The problem is the conversation and the way it lays claim to limited cognitive resources.

This finding is completely counter-intuitive: how can it endanger your life to carry on a simple, everyday task like a conversation? The survey clearly demonstrates that people’s perception of danger is completely divorced from the actual danger.

When driving, you are piloting a power plant, and a single mistake will cause human beings to die. This is a task worthy of your full attention.

Job Interview Notes

A message I sent to the students of CSC 290 (Communication for Computer Scientists) following our class with practice job interviews.

—

Alexey said: “I wish I had not used `you know`, `er`, and `well` that often. But these words come out instinctively, as they give me time (a second or two) to think of what I am going to say next. Still, there must be other ways.“

Here is a golden phrase to memorize when you need time to think about your answer for a question:

“That’s a good question. Let me think for a moment so I can give you a really good answer.“

You can also buy time by paraphrasing their question:

“Just so I know exactly what you’re asking—Are you saying <your interpretation of their question>?“

Of course, if you use one of these on a question that should be easy, you’ll seem dumb. And you probably can’t get away with using either more than once.

—

Yanshuai mentioned that he should have prepared a pitch for his computer vision project. In fact, you should prepare a pitch for every single item on your resume. You should also prepare pitches about your experiences in overcoming challenges or conflict, working in a team, dealing with difficult people, disagreeing with a superior or with your team, and so on.

You shouldn’t memorize your pitch, though. Instead, think about and jot notes about the importance of the project, the transferable skills you learned, and how it might relate to work at X.

Lots of interviewers also ask, “Tell me about yourself.” “Why do you want to work for X?” “Why should we hire you?” “Where do you see yourself in 5 years?” There is no excuse for stumbling on any of these questions. To answer them, remember: They are asking you to tell them about a personal quality or skill set. Lil gives an example: “I can multitask and meet difficult deadlines.  For example, when I was taking X course, I was also working on project Y, and had deadline W at work.  I was able to complete all these tasks by ___ and ___ resulting in ___.“

—

George mentioned doing mock interviews with a friend. This is a great idea.

—

Leon said, “I practiced answering questions rather than exploring my experiences themselves. If I had another chance, I would have prepared differently by studying past experiences and situations carefully.”

This is an important point and applies to more than just job interviews. Once you have developed good foundational speaking skills, your best preparation is not canned answers or prepared words. Your preparation will be about becoming deeply comfortable with your material. Then the words will flow.

—

Reza asked, “Also knowing that the interview is short should I let him ask more questions?“

While it is important to give complete answers, you should always leave the interviewer in control. In fact, you should carefully listen to the interviewer to get a sense of whether he or she wants you to take charge and talk a lot, or whether he or she would prefer to move things along.

In an interview, it may feel as if you should talk a lot, but in fact a big part of the game is listening to your interviewer (deeply listening—not just staying quiet while they talk.) You should listen actively—for example, paraphrase them and ask questions. Another one from Lil: “So you’re saying that the researcher wants you to organize data on 10,000 irradiated mice and you’re looking for a way to automate it:  Is that it? What are the resource constraints?“

I should probably explain “deep listening” better. Here’s an attempt.

We learn as children that “listening” means not interrupting the other person.

“Active listening” is a skill that involves engaging the other person in a conversation to gain a clear understanding of what they are saying.

When I say “deep listening,” I mean listening with all your brainpower. It’s hard work. You’re not just trying to understand their words. You’re also analyzing: What are they saying between the lines? How does this person feel? What’s going on in the back of their mind? What are they like as a person? What’s important to them?”

Actually, if you do this, you won’t need to worry about eye contact or eager posture since it will come naturally.

—

Taking a step back, the best way to prepare for an interview has nothing to do with interview questions, practice, listening or public speaking skills. To prepare, you must build your resume. By that I don’t mean putting words on page. Build your resume means actively seeking out new experiences. Then you can put them on your resume. Joining and contributing to team projects boosts your leadership and teamwork credentials. Writing open-source code outside of class is one of the best ways to get new technical experiences.

—

And of course,

• Smile
• Make eye contact
• Shake hands firmly
• Sit up
• Judo: Turn weakness into strength
• Give complete answers
• Demonstrate enthusiasm and confidence
• Be specific

Most importantly:

• Prepare
• Listen to your interviewer

If you have other important comments that I’ve missed, please let me know.

Your loquacious TA,

Aran

Units 0.03 is out

Units 0.03 is on PyPI. It includes a bug fix (A litre is a cubic decimetre, not a cubic centimetre!). It also has patches for PyPy that let you run a Python interpreter with built-in support for quantity literals. With these patches you can type 239 cm * cm * cm + 0.502 L and it will do what you expect.

Improve your Java: Restrict public methods to interface implementations

Good programming is all about restriction and constraint. Most possible programs do nothing interesting. Type systems, unit tests, code reviews and all that jazz are just ways of throwing out uninteresting programs.

Here is a chart view.

Notice the thickness of the blue slice.

This realization is what makes me feel comfortable proposing that we, as programmers, should mostly be thinking about new ways we can restrict ourselves.

Here is one way you can restrict your Java (or other classical Object-Oriented) programs to make them better.

Aran’s Public Interface Implementation Rule: All public methods on a class should be implementations of some explicit interface.

See, Java is tricky, because interfaces are interfaces, but so are classes. When you expose public methods on a class that are not part of an interface, a user of the method must specify the class name on the left hand side:

YourClassThatsAlsoAnInterface p = new YourClassThatsAlsoAnInterface();

That means their code is now glued to your implementation. They might not like that.

It would be better if you let them code to an interface and substitute implementations as desired. Thus, don’t bother putting public methods on a class that aren’t implementations of an interface.

YourInterface p = new yourClassThatsJustAClass();
YourInterface p2 = new theirReimplementationOfYourClassThatsJustAClass();

Incidentally, this rule totally enables Joshua Bloch’s Effective Java, Item 34: Refer to objects by their interfaces.

As Zak points out, the tradeoff is that you may hurt some compiler optimizations. If you have benchmarks to prove that following this rule causes unacceptable performance degradation, you have my permission to ignore it. Thanks for asking.

Leave your thoughts in the comments.

A Hack for “Unknown or expired link”

You’re using a web site, you fill in a big long form, and, upon submitting, you get “Unknown or expired link.” Why does this happen? The short[1] explanation is that the web site gave you a hidden code to submit with your form, and while you were typing, your code expired. If you hit “Refresh” to get a new code, your browser will probably throw away the contents of your form. You’re lucky you can hit “Back” from the “Unknown or expired link” page.

What to do?

Choice number 1: Copy out your form contents, refresh the page, and paste the contents back in. Slow, boring and painful.

Choice number 2: Get a new code and edit your current page to use it. Fast and fun!

How to get a new code: Open a new browser window, go to the page with the form, use “View source”, find the form you were filling in, find the hidden field in the form with a weird value, and copy the strange value.

How to edit your current page to use it: Go to the window where you’ve filled in your form, pop open your DOM editor, find the weird hidden field, and replace its value with your new code. Now when you submit your form, it will be using a fresh code!

OK, so maybe it isn’t easy, but at least it’s fast.

[1]: The long explanation is that the web site is probably using a continuation-passing style for managing client-side state and it garbage-collected your form submission continuation. I didn’t think you wanted to know.

11 Proposals For A Better DCS

Incoming graduate chair Peter Marbach has been leading a series of “town hall” meetings to build support for a Department of Computer Science conference, organized by graduate students. The stated goals are a) to improve the quality of the graduate experience by promoting interaction between areas and b) to build the Department’s reputation in industry and academic circles by effectively communicating our successes.

I’m happy that Peter cares enough to try something big, and doubly happy that he is trying to listen to us and involve us in his work. But, while the goals are lofty, I don’t agree with the conference approach. I think that there are lightweight projects that would contribute more to interaction between areas, and I feel that hosting a conference is not an effective way to build a reputation for our work. I would also criticize the proposal for lacking specificity (especially budget, organizing team, schedule) and accountability (no simple success or failure metrics).

I hate to criticize an ambitious and well-meaning proposal without suggesting a replacement. Accordingly, I’ll list some specific proposals below, without taking credit for any of the ideas. Before I do, I’ll list a few of the community projects that already exist.

CSGSBS “big” events

The CSGSBS hosts large, general-interest events a few times a year, including an upcoming BBQ.

CSGSBS “little” events

The CSGSBS coordinates weekly events including cookie breaks, movie nights and pub nights. There are attended by a small percentage (~10%) of the grad student population, and most weeks attract the same crowd again and again.

Research-In-Action showcase

Annual conference hosted on campus with posters and demos from graduate students. A few industry and political guests attend. Typically heavily-weighted on demo-friendly DGP work.

Area talks

Most areas have weekly talks. Outsiders are welcome in theory, though an interested outsider would have to find the right mailing list and often the material is too advanced.

11 Proposals for a better DCS

1. Lightweight talks

1.a Area intro talk series

Each week, a different area gives the talk. At a casual, accessible level, the talk covers why the area is interesting, the most important fundamental closed and open problems in the area, key results, and how each student’s work in the area fits into the big picture. The speaker recommends a survey paper or introductory book for students interested in more. No posters, textual or mathematical slides or chalkboards allowed. Each proposed speaker is vetted by the organizer in advance to ensure a minimum public speaking ability.

1.b Monthly Mini-TED

Monthly casual 1.5 hour meeting in a lounge with coffee. Three speakers give TED-format talks on any beginner-accessible topic of intellectual interest not related to research. Given the variety of DCS member  interests, we might hear talks on sailing, design, typography, photography, quantum physics or hooping.

2. DCS Portfolio

2.a Technical reports database

For communication of early or small research, revive the technical reports database or provide a DCS-only externally-accessible site with ArXiv.org-like functionality.

2.b Publication portfolio

Provide an RSS feed with links to PDFs of all research published by members of the department.

2.c Entrepreneurial portfolio

Directory of links to companies founded by members of the DCS.

2.d Graduate success portfolio

Track the alumni network (undergrad and grad) and list the institutions they join after graduation.

2.e Blogroll

Directory and firehose of blogs written by DCS members. Michael Famelis has done good work in this area for software engineering. Extend it to the whole department. For bonus marks, provide a global WordPress installation on cs.toronto.edu for DCS member blogs.

2.f Software Portfolio

Directory of software written by members of the DCS. This can include side projects, not just research-related work. Subsection for links to software to which DCS members have contributed. For bonus marks, provide distributed version control hosting and virtual private servers for hosting web applications.

3. Activities

3.a Pick-up sports

Weekly pick-up soccer or ultimate games in the summer and fall.

4. Structural Changes

This is the controversial stuff.

4.a Common-space whiteboards

Move whiteboards out of private, locked areas into public spaces where they can foster discussion.

4.b Cooperative Education

This is the biggest proposal, with highest cost and greatest potential payoff.

For the undergraduate program, provide institutional support for four-month co-op terms after first and second years, and break the PEY into four consecutive four-month terms. For MSc students provide institutional support for corporate research internships between completion of class and start of full-time research. A common infrastructure would support both missions.

Why co-op?

Work terms create a feedback loop with in-class education. Real work motivates study and supports good study habits. Programming skills honed in the workplace support theoretical learning in the classroom or lab. Problems discovered in the workplace can motivate research directions.

Work terms create a feedback loop with companies. With more work terms, more companies see more students. Companies are eager to hire students who have worked for them before. Companies are willing to contribute resources to universities that are an important part of their HR pipeline.

Weakness of the PEY

I claim the following weaknesses in the PEY program:

1. Students make long-term commitments on their first-ever round of interviews. They often have no professional experience, and thus are not yet qualified for advanced work. This restricts their placement to beginner work, but this commitment to beginner work lasts sixteen months instead of four.
2. Some students just don’t fit with some companies. In other cases, companies abuse long-term students for menial tasks such as manual testing. Four-month terms salvage these cases, without restricting a student from returning to a particularly good employer.
3. Students see only one corporate culture, team culture and set of engineering practices before graduation.
4. Students only get one round of interview practice.

Each of these is addressed in the proposed model.

Addressing Common Objections to Co-op for the DCS

I have heard two common objections.

1. Prohibitive cost: This program would be expensive to administer. Answer: Everyone wants something for nothing, but a program like this is an investment. Other universities successfully mitigate the costs of similar programs. We can do it too.
2. Competition with Waterloo (and other schools): The implementation of such a program would position us as an alternative to Waterloo’s well-oiled machine, and would contradict our years of PEY marketing. Answer: We should neither hesitate to adopt a good idea just because someone else uses it, nor should we allow a desire for self-consistency to trump innovation.

Summary

A frank an open discussion of ideas might lead us to improvements in our graduate experience, provided we restrict ourselves to specific proposals with measurable outcomes.

Here’s where you come in

1. Leave a comment in your real name, and rank the ideas you care about. “++” for “would actively support, defend and work for”, “+” is “support passively”, “-” is “oppose passively” and “–” is “would actively oppose, attack and work against.”  So if you hate co-op, dislike pick-up sports, but would fight to see whiteboards moved to hallways, write this: [3.a-, 4.a++, 4.b--].
2. Go find someone else in the department, force them to read this through and make them think through their opinions on these issues.

Let’s send a message about how to make our department better.