[PYTHON] Allocation of resources to testing

what is this

You are a software tester. The software is made up of four modules. The tests are also divided into five categories: A, B, C, D and E.

Now you have to decide which category of tests to do and at what rate.

policy

From past experience, assume that the module-test correlations are given in Table A. At this time, when each test is performed at a ratio of $ p $, the probability of finding a failure in module 1 shall be calculated at $ A_1 \ cdot p $.

Data creation

Make Table A with random numbers.

python3

import numpy as np, pandas as pd
from pulp import *
from ortoolpy import addvar, addvars
np.random.seed(4)
nm, nt = 4, 5 #Number of modules, number of tests
A = pd.DataFrame(np.maximum(np.random.normal
     (-0.05,0.2,(nt,nm)), np.zeros((nt,nm))),
     columns=['module%d'%(i+1) for i in range(nm)],
     index=['test%s'%chr(i+65) for i in range(nt)])
print(A)

Calculation part 1

Let's calculate in Python to maximize the total sum.

python3

m = LpProblem(sense=LpMaximize) #Mathematical model
x = addvars(nt) #variable(Test percentage)
m += lpDot(A.sum(1),x) #Objective function
m += lpSum(x) == 1
m.solve()
r = np.vectorize(value)(x)
print('%s sum%.4f minimum%.4f'%(LpStatus[m.status],
    r.dot(A.values).sum(), r.dot(A.values).min()))
for i,v in zip(A.index,r):
    print('%Work load of s%.4f'%(i,v))
>>>
Optimal sum 0.3541 Minimum 0.0000
Test A workload 0.0000
Test B workload 0.0000
Test C workload 0.0000
Test D workload 0.0000
Test E workload 1.0000

The result is that only the most efficient test E is done.

Consideration

Test E cannot find any failures in Module 1 and Module 2. Quality assurance is considered to guarantee the quality of a certain minimum line.

In "Calculation # 1", we test to improve the average quality like the distribution of red, but we still have the possibility of low quality. As "Calculation # 2", consider a test that avoids low quality rather than increasing the average quality as in the blue distribution.

Calculation part 2

Let's calculate to maximize the minimum quality per module.

python3

m = LpProblem(sense=LpMaximize) #Mathematical model
x = addvars(nt) #variable(Test percentage)
y = addvar() #variable(Lowest line)
m += y + lpDot(A.sum(1),x)*0.01 #Objective function
m += lpSum(x) == 1
for j in range(nm):
    m += y <= lpDot(A.ix[:,j],x)
m.solve()
r = np.vectorize(value)(x)
print('%s sum%.4f minimum%.4f'%(LpStatus[m.status],
    r.dot(A.values).sum(), r.dot(A.values).min()))
for i,v in zip(A.index,r):
    print('%Work load of s%.4f'%(i,v))
>>>
Optimal sum 0.0948 Minimum 0.0237
Test A workload 0.1434
Test B workload 0.0000
Test C workload 0.2838
Test D workload 0.5435
Test E workload 0.0293

Compared to Part 1, the sum was lower, but the lowest was higher.

This idea is the same as "Solving game theory with optimal combination".

that's all