Good lecture notes on the current state of the art in data structure research.
Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structures: TIME TRAVEL We can remember the past efficiently (a technique called persistence), but in general it’s difficult to change the past and see the outcomes on the present (retroactivity). So alas, Back To The Future isn’t really possible. GEOMETRY When data has more than one dimension (e.g. maps, database tables). DYNAMIC OPTIMALITY Is there one binary search tree that’s as good as all others? We still don’t know, but we’re close. MEMORY HIERARCHY Real computers have multiple levels of caches. We can optimize the number of cache misses, often without even knowing the size of the cache. HASHING Hashing is the most used data structure in computer science. And it’s still an active area of research. INTEGERS Logarithmic time is too easy. By careful analysis of the information you’re dealing with, you can often reduce the operation times substantially, sometimes even to constant. We will also cover lower bounds that illustrate when this is not possible. DYNAMIC GRAPHS A network link went down, or you just added or deleted a friend in a social network. We can still maintain essential information about the connectivity as it changes. STRINGS Searching for phrases in giant text (think Google or DNA). SUCCINCT Most “linear size” data structures you know are much larger than they need to be, often by an order of magnitude. Some data structures require almost no space beyond the raw data but are still fast (think heaps, but much cooler).(via Tim Freeman)
At least in terms of StackOverflow rep:
For the first part of the study, the researchers compared the age of users with their reputation scores. They found that an individual’s reputation increases with age, at least into a user’s 40s. There wasn’t enough data to draw meaningful conclusions for older programmers. The researchers then looked at the number of different subjects that users asked and answered questions about, which reflects the breadth of their programming interests. The researchers found that there is a sharp decline in the number of subjects users weighed in on between the ages of 15 and 30 – but that the range of subjects increased steadily through the programmers’ 30s and into their early 50s. Finally, the researchers evaluated the knowledge of older programmers (ages 37 and older) compared to younger programmers (younger than 37) in regard to relatively recent technologies – meaning technologies that have been around for less than 10 years. For two smartphone operating systems, iOS and Windows Phone 7, the veteran programmers had a significant edge in knowledge over their younger counterparts. For every other technology, from Django to Silverlight, there was no statistically significant difference between older and younger programmers. “The data doesn’t support the bias against older programmers – if anything, just the opposite,” Murphy-Hill says.Damn right ;)
Test Double is a generic term for any case where you replace a production object for testing purposes. There are various kinds of double that Gerard lists: Dummy objects are passed around but never actually used. Usually they are just used to fill parameter lists. Fake objects actually have working implementations, but usually take some shortcut which makes them not suitable for production (an InMemoryTestDatabase is a good example). Stubs provide canned answers to calls made during the test, usually not responding at all to anything outside what’s programmed in for the test. Spies are stubs that also record some information based on how they were called. One form of this might be an email service that records how many messages it was sent. Mocks are pre-programmed with expectations which form a specification of the calls they are expected to receive. They can throw an exception if they receive a call they don’t expect and are checked during verification to ensure they got all the calls they were expecting.