Should CS students be graded on performance? (opens in new tab)

It depends on the context of the problem:

* If it's being framed as being applied to a small number of items on one machine, I'd mark down for premature optimization. Normally such assignments have a specific point, and going off the deep end with efficiency measuring often misses it.

* If it's being framed as being applied to huge data sets, asymptotic complexity is the metric to go off of.

* If it's being framed as a latency-dependent (UI, servers) then actual timing is the metric to grade off of.

I'd say that if you have an algo that is faster for small numbers, it usually doesn't really matter that it's faster. Small numbers go fast no matter what. Unless it's latency dependent, it's encouraging students to work on things that don't matter at all.

EDIT: spacing EDIT2: spelling

First, find a better color scheme for something you intend people to read. That was painful.

Second, when I was a CS student I expected to be graded on my response to a particular problem. If you wanted me to roll my own sort, I expect that to be specified. If you want me to explain in comments why I chose certain algorithms say something in class. I'm not going to waste time implementing an efficient sorting algorithm for a 100 element list (or 1000 even) just to provide a TA with a nerdgasm.

I feel this article is asking if students should be graded on an arbitrarily deep rubric. I had enough to worry about as an undergrad without caring if a bored TA was going to dock me 10 points for calling Arrays.sort.

The ACM contest has it right: specify the size of the input and the running time available. The program must be able to run an input up to that size in the given runtime. That's it: no more, no less.

At Harvey Mudd College, we have a class based on the ACM which works similarly, though with relaxed runtime constraints: 42 minutes per problem. In practice, you rarely need more than a few seconds, but the 42 minute cutoff (combined with large problem sizes) is a great way to prevent people from using brute-force approaches when there's a smarter option available.

I had a CS Professor who was convinced that he had cheaters in the class(1), so he decided we should all be graded on performance of our programs. This resulted in a lot of stress for a lot of students (C code only, most students taught only Pascal/Modula-2) and some seriously nasty things done in the programs. I rate it a big fail for what we were doing.

I can actually kind of see this for embedded / small device programming. Embedded systems that can't meet their performance goals are useless and buying faster hardware is very costly option.

(1) he also believed not one of us submitted a C program to print "Hello, World!" that worked. It was a 8AM class and I had just driven 120miles (home visiting) through a blizzard to make it to class when he announced this fact in a dramatic fashion. I required calming down.

Aside from teaching big O notation, I would be skeptical of grading for performance. Teaching people that the performance code they write to solve a toy problem matters might lead them to go into industry and then perform microoptimizations on code to solve performance problems when they are virtually universally caused by the interaction of many moving parts.

If you're doing web apps, application code is close to the last place I'd reasonably expect there to be severe performance issues. You're far, far more likely to either blow something architecturally ("We call a foreign API in the request/response cycle.") or flub settings where simple best practices produce repeatable results ("It is 2011 -- do you know where your gzip is?")

YES. My Data Structures and Algorithms class was graded by benchmark (ECS 60, Sean Davis, UC Davis). Being graded this way was one of the most valuable collegiate experiences I've had (I'm a fourth year on the way out).

Sean gives his students two "challenge programs" a quarter. The first program is usually ultimately a matter of picking/optimizing the right data structure. We do graph algorithms by the 2nd half and so the later challenge problem is inevitably some graph algorithm.

The challenges are close to real world problems. For ex., our 2nd challenge program was to determine the maximum # of donations Obama could receive at any one time given a capacitance graph of his website's network topology (I took the class around the '08 election). We had to design, implement, and test everything by ourselves.

I don't agree with the naysayers who suggest that grading this way doesn't teach the really important software engineering tasks. You really had to feel out the problem domain in order to get the best optimization, and it the book answer wasn't always the best answer. Optimization is about a lot more than simply picking the right data structure or algorithm.

Partnering with the crazy russian genius with an 8086 instruction set in his head that quarter made me the C programmer I am today.

TL;DR Grading by benchmark makes students better programmers.

No, they should not. Unless performance was one of the assignment requirements.

Performance is rarely on the most important requirement in software development.

If you don't believe me, watch Prof. Leiserson make this point.

http://ocw.mit.edu/courses/electrical-engineering-and-comput...

Define performance. The question is ill formed since there is nothing specified to measure. The example wonders who should get the better grade based upon design decisions each of which are defensible in some context. And it's the asymptotic and/or typical behavior that is touted, not the implementation.

At a most elemental level, a program written by a CS student should work, that is, do what it is supposed to do for all legal inputs and provide meaningful diagnostics when it fails. That's hard, but that is the nature of programming.

Finding and choosing algorithms to accomplish a task (well defined or not) is also computer science. That is algorithmics and design. Doing a good job here is harder to measure and depends both a deep understanding of the available building blocks and the problem at hand. Moreover, trade-offs are involved and need to be resolved even though there is little real data to guide decisions. Seeing and exploiting invariant properties of the problem often enables better performance in some dimension; insight there is often called cleverness.

Equally important is the ability to see complex things in a simplified way by introducing abstractions. That's the only way to solve difficult, complex problems, and it is exceptionally difficult to codify.

Generally, performance isn't interesting in cases where asymptotic running time isn't dominant. You could sort 3 items with a couple of `if` statements, which would compile to a handful of microprocessor instructions. That's not interesting. Surely, in any data structure or algorithms course offered as part of a CS degree, the sorting assignments are for the "general sorting" problem. This implies relatively large data sets (and unpredictable, since you don't expect to be able to use a faster non-comparison sorting algorithm).

No. Stuff like this only comes with experience and it's more important to teach students about the fundamental and overarching themes of computer science than to pester them with optimization issues.