time complexity for code and an order of magnitude improvement -

i have following problem:

1. for following code, reason, give time complexity of function.

2. write function performs same task order-of magnitude improvement in time complexity. function greater (time or space) complexity not credit.

code:

int something(int[] a) {     (int = 0; < n; i++)         if (a[i] % 2 == 0) {              temp = a[i];              for(int j = i; j > 0; j--)                 a[j] = a[j-1];              a[0] = temp;         } } 

i'm thinking since temp = a[i] assignment in worst case done n times, time complexity of n assigned that, , a[j] = a[j-1] run n(n+1)/2 times time complexity value of (n2+n)/2 assigned that, summing them returns time complexity of n+0.5n2+0.5n, removing constants lead 2n+n2 , complexity of n2.

for order of magnitude improvement:

int something(int[] a) {     string answer = "";     (int = 0; < n; i++) {        if (a[i] % 2 == 0) answer = a[i] + answer;        else answer = answer + a[i];     }     (int = 0; < n; i++)        a[i] = answer.charat(i); } 

the code inside first for-loop executed n times , in second for-loop n times, summing gives time complexity figure of 2n.

is correct? or doing wrong?

i suppose function arrange list numbers @ beginning of list , followed odd numbers.

for first function complexity o(n²) have specified.

but second function complexity o(n) if operator + used appending implemented constant time operation. append operator + implemented constant time operation without hidden complexity. can conclude second operation takes o(n) time.