Sub-Model 1: |
Step 1. Generate the MMRE (M) for Available Projects using actual and COCOMO estimated efforts. |
(i) [BEGIN] |
(ii) Input the 15 cost drivers, KLOC, Actual Effort for NASA projects. |
(iii) [LOOP] |
for to no. of projects (say ) |
EAF[] = D1 * D2 *⋯* D15 |
Estimated Effort[] = [] * (kloc[]∧ []) * EAF[] |
MRE[] = /Actual Effort[] |
MMRE (original) += MRE[] |
MMRE (original) /= [The original MMRE is obtained and noted down] |
(iv) [END OF LOOP] |
(v) [END] |
Sub-Model 2: |
Step 2. for to 15 |
temp = Emi |
Set Emi = 1 |
Calculate Influenced MMRE(MN) |
List[] = ; |
List[] = MN~M; |
Emi = temp; |
end for |
Sub-Model 3: |
Step 3. Sort the list according to the second parameter in descending order |
For to 14 |
For to 15 |
If (list[] < list[]) |
then |
swap (list[], list[]) |
end if |
end for |
end for |
Step 4. Sig = list[] represent the order of Significance occurrences. |
Sub-Model 4: |
Step 5. for to 15 |
for = very low to Extra high (Six rating of cost driver) |
Select Projects (P) as an input for calculating the fitness value using fitness function F1 = MMRE(P). |
Set the range R as {Rmax, Rmin} |
Generate initial population for the cost driver with Range R. |
performs The Genetic operations for K generations. |
(1) Tournament Selection |
(2) Crossover with Pc = 0.8 |
(3) Mutation with Pm = 0.3 |
Select the individual (CDNEW) with the best MMRE |
Step 6. Calculate the MMRE(Mmod) by replacing CDNEW with CDij |
if (Mmod < M) |
then update the value of CDij and M. |
else |
discard the value |
end if |
end for |
end for |