there is this quote from Clean Code by uncle Bob that I simply love.
'Avoid mental mapping: In general programmers are pretty smart people. Smart people sometimes like to show off their smarts by demonstrating their mental juggling abilities. After all, if you can reliably remember that r is the lower-cased version of the url with the host and scheme removed, then you must clearly be very smart.'
you can't do that. when you implement search you need a global view over the collection to understand what are the most relevant documents. You are describing the paradise of the spammers :)
Hey, I feel you - and I understand the pressure - i have been in that situation. The point is that this:
> you sometimes have to write an experiment with a deadline next week.
shouldn't happen. And yes, at the moment is like this - sometime you will have to hack. But if the all community start to push for proper practices, instead of just saying "is as it is" - there will be less papers, with more quality.
Lol I spent 5 years in academia, and I have a PhD in CS - I know what I'm talking about. Specs of code change in academia as in industry, I was able to write unit tests and document my code also in academia. And I know in medicine and chemistry time to publish are much longer - but that is not connected with the fact that they know how to properly use a microscope, clean the lab, and keep an inventory.
If you don't write unit tests how do you hedge the possibility of having bugs in your code?
> The job of a scientist is really not to ship software, that's what a team of engineers would do.
I think that this is the real problem - in academia there is this idea that learning good practices is like a 'dirty' thing that is not required, while instead it would speed up the work and make it more reliable. if you look at chemistry or medicine, there researches have good practices for managing the lab and respect them.
"a general search can be machine learning" I don't get this sentence: Machine learning is about building a mathematical model of sample data, known as "training data".