I heard some guys telling that the use of
rand() is bad EVEN AFTER USING
srand() to get a seed. Why is that so? I want to know how the stuff happens... And sorry for another question.. but what is an alternative to this then?
There are two parts to this story.
rand is a pseudorandom number generator. This means it depends on a seed. For a given seed it will always give the same sequence (assuming same implementation). This makes it not suitable for certain applications where security is of a great concern. But this is not specific to
rand. It's a problem of any pseudo-random generator. And there are most certainly a lot of classes of problems where a pseudo-random generator is acceptable. A true random generator has it's own problems (efficiency, implementation, entropy) so for problems that are not security related most often a pseudo-random generator is used.
So you analyzed your problem and you conclude a pseudo-random generator is the solution. And here we arrive to the real problems of the C random library (which includes
srand) who are specific to it and make it obsolete (a.k.a.: the reasons you should never use
rand and the C random library).
One problem is that it has a global state (set by
srand). This makes it impossible to use multiple random engines at the same time. It also greatly complicates multithreaded tasks.
The most visible problem of it is that it lacks a distribution engine:
randgives you a number in interval
[0 RAND_MAX]. It's uniform in this interval, which means that each number in this interval has the same probability to appear. But most often you need a random number in a specific interval. Let's say
[0, 1017]. A commonly (and naive) used formula is
rand() % 1018. But the problem with this is that unless
RAND_MAXis an exact multiple of
1018you won't get an uniform distribution.
Another problem is the Quality of Implementation of
rand. There are other answers here detailing this better than I could, so please read them.
In modern C++ you should definitely use the C++ library from
<random> which comes with multiple random well-defined engines (for integer and floating point types) and various distributions.