Especially in the the more experimental subfields of computer science like systems, how often are results faked? If there is no verification process for any code used, do researchers sometimes fake results to save time?
Nobody knows. Unless someone tries to reproduce the results and cannot, there's not ever even going to be a challenge to the results. Direct reproduction in CS and similar fields is generally done in papers that extend older results or propose a new method. To my knowledge, we do not see a lot of retractions based on these kinds of studies, so I'd say the rate of faking is low.
Retraction Watch's CS section has about 15 articles in it, but most of them appear to be about retractions for plagiarism not for faking results.
Let me split my answer into two sub-areas: reputable venues and crap venues.
In reputable venues, it is just as possible for somebody to commit fraud (or any of the other deadly sins of science) in experimental computer science as in any other experimental science. It also appears to be quite rare, because there is usually pretty clear observability and a pretty clear relationship between theory and practice in computer science (unlike, say, certain subfields of biology) and so fraud would often be relatively easy to detect. More to the point, however, the risk/reward tradeoff is terrible: one detected incident will likely destroy a career.
In crap venues, there might well be constant fraud---but who cares? If they'll accept machine-generated papers, they might accept anything, and I'm probably not going to cite it or even look at it in any case.
There are quite a few interesting articles on the topic of fraud rates. I didn't see anything specific to computer science, but the general sense is that it's very difficult to determine.
It's very tough to actually detect fraud. Most cases (that I'm familiar with, at least) involve someone falsifying data in a highly active field and publishing earth-shattering results that turn out to be false. As was said in other answers, detecting the fraud requires attempting to reproduce the results, failing, and then determining that the problem is on the other end.
With that said, there aren't many good proxy measures of fraud. Retractions are a start, but they're far and few between. We can almost be certain that not all fraud is retracted. Surveys can be used but they're also notoriously inaccurate. There really aren't many other ways to measure that would actually provide a useful metric.