Given the project data below:

$\backslash$table$[[$project$,$inputs,outputs,entity accesses,system users,$\backslash$table$[[$program$-],[$ming$],[$language$]],],[1,210,420,40,10,x,30],[2,469,1406,125,20,x,85],[3,513,1283,76,18,y,108],[4,660,2310,88,200,y,161],[5,183,367,35,10,z,22],[6,244,975,65,25,z,42],[7,1600,3200,237,25,y,308],[8,582,874,111,5,z,62],[x,180,350,40,20,y,],[Y,484,1190,69,35,y,]]$

Note $x$ and $Y$ are new projects for which estimates of effort are needed.

A$.$ What items are size drivers?

B$.$ inputs, outputs, entity accesses $($system users for certain aspects$)-$ the more of these there are the larger the development job

C$.$ What items are productivity drivers?

D$.$ Programming language $-$ the number of lines of code that can be produced in a day will depend, in part, on the programming language

E$.$ What are the productivity rates for programming languages $x,y$ and $z?$

F$.$ What would be the estimated effort for projects $x$ and $Y$ using a Mark II function point count?

G$.$ What would be the estimated effort for $x$ and $Y$ using an approximate analogy approach?

H$.$ Project X seems closest to Project $5$ which provides an estimate of $22$ days, and Project Y seems to be closest to Project $3$ which gives an estimate of $108$ days

I. What would have been the best estimating method if the actual effort for $x$ turns out to be $30$ days and for $Y$ turns out to be $120$ days? Can you suggest why the results are as they are and how they might be improved.