APPENDIX A: ORGANIZATION CHART ACHMEA

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Appendix A: Organization Chart Achmea 72

APPENDIX A: ORGANIZATION CHART ACHMEA

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Appendix B: Organization Chart Achmea Active 73

APPENDIX B: ORGANIZATION CHART ACHMEA ACTIVE

Figure B-1: Organization Chart Achmea Active

Active

CTO

Central Technology Office

F&C Finance & Control

Secretariaat

CS Customer Services

SDMC System Development &

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Appendix C: Organization Chart Achmea SDMC 74

APPENDIX C: ORGANIZATION CHART ACHMEA SDMC

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Appendix D: System Architecture of IRIS 75

APPENDIX D: SYSTEM ARCHITECTURE OF IRIS

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Appendix E: Data Model of IRIS 76

APPENDIX E: DATA MODEL OF IRIS

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Appendix F: Connection Models 77

APPENDIX F: CONNECTION MODELS

The GAST tool, written in Clean, has been connected to a dll version of the ‘Actuarieel Formuluarium’. The connection is realized by using the clean model named Calldll (see Figure F-1).

Figure F-1: Connection GAST and AF – Implementation Module

First of all a file, called ‘Actuarieel’, is imported in the Calldll model. This file consists of the name of the dll (mpaform) on the first line. The second line of the file consists of the action needed to start the dll (ACTBER). The actber in Figure F-1 is defined to generate an input statement (idata) and an output statement (udata). The idata is used to put data into the system. The udata is needed to store the output that is presented by the ‘Actuarieel Formularium’.

To import this implementation module in the testing models (Figure F-3) a so-called

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Appendix F: Connection Models 78

Figure F-2: Connection GAST and AF – Definition Module

The actber will be used when testing the different functions. As shown in Figure F-3 (same code is shown in Figure 6-5) the idata will first be given to the ‘Actuarieel Formularium’. The idata consists of all the input parameters (58 input parameters in total). The ‘Actuarieel

Formularium’ presents the results, which will be stored in udata. The udata can be compared to the results calculated by the models of the functions that use the same input values.

Figure F-3: Testing the Function with GAST

More information about the working of GAST and Clean can be found in the articles written by Koopman et al. (2002), Plasmeijer and van Eekelen (version 2.0), Koopman and

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Appendix G: Testing Vormcodes with GAST 79

APPENDIX G: TESTING VORMCODES WITH GAST

This part describes all the seven steps that must be realized to test a certain vormcode in the ‘Actuarieel Formularium’ with the GAST tool. As example, vormcode 100 will be used.

Description Vormcode:

The functions 100-1, 100-2, 100-3 and 100-4 are presented in Figure G-1. Based on this figure, the different output parameters can be calculated. Chapter 6 describes the different phases and output parameters in more detail.

Type Phase Vormcode Output 1 2 3 4 5 6 7 90 100 NK 100-1 100-2 100-4 100-4 100-1 100-2 NSK 100-1 100-4 NRK LF 100-1 100-2 100-4 100-4 100-1 100-2 BF 100-3 100-3 RF-1 RF-2

Figure G-1: Vormcode 100 – Pension of Old Age

The figure presents a matrix which shows which function must be used to calculate a certain output parameter in a certain situation. For example, the output variable NK for phase one (insurer pays the premiums) can be calculated by using function 100-1.

Step 1: Define input parameters

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Figure G-2: Input Parameters – Implementation Module

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Appendix G: Testing Vormcodes with GAST 81 parameter OpslToekAdminK three different types are defined to use the input value as

integer, real and string when needed.

To import this module in other modules a so-called definition module must be made (see Figure G-3). These definition modules must be made every time a module needs to be imported in another module. This means that every module needs a definition module, with exception of the test module that represents the actual input values and the different test cases (see step 6).

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Step 2: Making models of general calculations

The second step is making models of general calculations that can be used in the modules presenting the functions. The first model presents all durations (see Figure G-4 for an example) and the second model present different chances (see Figure G-5 for an example).

Figure G-4: Duration Model

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Step 3: Making models of the different functions

The third step is making models of each function described. In Chapter 6 an example has been given of function 100-1 (see Figure 6-2).

Step 4: Making models of each output parameter

The fourth step is making for each output parameter a different model. By doing this it is possible to test each output parameter separately when necessary. In Chapter 6 an example of the output parameter NK has been given (see Figure 6-3).

Step 5: Making the connection module

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Step 6: Making the test module

The sixth step is making the test module. This module consists of the actual values that can be put in the input parameters and the different tests. Figure G-6 and G-7 shows a part of the testing module.

Figure G-6: Test Module – Input Values

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Figure G-7: Test Module – Test Code

Figure G-7 shows the properties for testing the ‘Actuarieel Formularium’. The property ‘propNettoKoopsom’ can be defined in two different ways. The first one generates the input and output values, but when an error occurs the test stops. The second one only show the input values, but the test stops when 10 errors occur, as defined by Start. With the function Start 900 test cases will be inputted into the ‘Actuarieel Formularium’. Only one of the properties can be defined in the test module. This means that you must change one type in another when you want to test differently.

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Figure G-8: Test Property to compare NK

An example of the output program is shown in Figure G-9. The first value is the output value NK generated by the Clean model. The second value between the quotes is the output value NK generated by the ‘Actuarieel Formularium’. As can be seen in the figure, the values do not match in this situation. The tests can only be run after the final step (step 7) is realized.

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Step 7: Making project to run the tests

The final step is making a project in the Clean system, so the tests can be executed

automatically by GAST. The project must have the same name as the test module. When the project is made, some options must be changed. First of all the application options must be set as follows:

- Maximum Heap Size: 1750M - Stack Size: 100M

- Next Heap Size Factor: 16 - Initial Heap Size: 200K

When the project is used for regression testing the application options are different:

- Maximum Heap Size: 300M - Stack Size: 10M

- Next Heap Size Factor: 16 - Initial Heap Size: 200K

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89

APPENDIX H: EXAMPLES TEST RESULTS

This part will give examples of two different faults that have been found during the tests done with GAST. The examples of the faults that have been found will be shown in tables that consist of the relevant input parameters, the output values generated by the ‘Actuarieel

Formularium’ (AF) and the output values generated by GAST. Ages and durations consists of a ‘,’. The value before ‘,’ means years and the value after ‘,’ means months.

Interpolation

Seven functions show differences between the values generated by the ‘Actuarieel Formularium’ and the values generated by GAST when interpolation is needed. For each function some examples of faults are generated and shown in a table.

- Function 650-1: This function uses interpolation on the input parameters ‘Lft1eVerz’,

‘EindLftUitk’ and ‘WachttijdTotIngUitk’. The differences in this function will only be generated when the input parameter ‘Lft1eVerz’ consists of years and months.

EindLftUitk 75,0 75,0 75,0 75,0 75,0 75,0 Lft1eVerz 50,1 50,3 50,5 50,7 50,9 50,11 WachttijdTotIngUitk 2,0 2,0 2,0 2,0 2,0 2,0 AODuur 2,0 2,0 2,0 2,0 2,0 2,0 Gesl1eVerz 1 1 1 1 1 1 RekenRente 3 3 3 3 3 3 Regl_indv_code 1 1 1 1 1 1 Correctie_factor 3 3 3 3 3 3 Input values Beroepsklasse_factor 2 2 2 2 2 2 Values AF 1,28862 1,29920 1,30845 1,31637 1,32296 1,32822 Output

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Appendix G: Testing Vormcodes with GAST 90 - Function 650-2: The differences within this function will only be generated when the

input parameter ‘Lft1eVerz’ consists of years and months.

EindLftUitk 75,0 75,0 75,0 75,0 75,0 75,0 Lft1eVerz 50,1 50,3 50,5 50,7 50,9 50,11 WachttijdTotIngUitk 2,0 2,0 2,0 2,0 2,0 2,0 AODuur 2,0 2,0 2,0 2,0 2,0 2,0 Gesl1eVerz 1 1 1 1 1 1 Input values RekenRente 3 3 3 3 3 3 Values AF 11,33978 11,30653 11,26930 11,22811 11,18295 11,13382 Output

values Values GAST 11,33433 11,29313 11,25194 11,21075 11,16956 11,12837

- Function 653-1: The differences within this function will only be generated when the

EindLftUitk 75,0 75,0 75,0 75,0 75,0 75,0 Lft1eVerz 50,1 50,3 50,5 50,7 50,9 50,11 WachttijdTotIngUitk 2,0 2,0 2,0 2,0 2,0 2,0 AODuur 2,0 2,0 2,0 2,0 2,0 2,0 Gesl1eVerz 1 1 1 1 1 1 RekenRente 3 3 3 3 3 3 Regl_indv_code 1 1 1 1 1 1 Correctie_factor 3 3 3 3 3 3 Input values Beroepsklasse_factor 2 2 2 2 2 2 Values AF 1,25642 1,26614 1,27483 1,28249 1,28914 1,29475 Output

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EindLftUitk 75,0 75,0 75,0 75,0 75,0 75,0 Lft1eVerz 50,1 50,3 50,5 50,7 50,9 50,11 WachttijdTotIngUitk 2,0 2,0 2,0 2,0 2,0 2,0 AODuur 2,0 2,0 2,0 2,0 2,0 2,0 Gesl1eVerz 1 1 1 1 1 1 Input values RekenRente 3 3 3 3 3 3 Values AF 11,07419 11,03970 11,00206 10,96129 10,91737 10,87032 Output

values of the input parameters are the following:

‘Lft1eVerz’ < 26,0

‘Lft2eVerz’ < 27,0

Months within ‘EindLftFinanc’ = 2 or 6

RekenRente 3 3 3 3 3 3 EindLftFinanc 62,2 62,2 62,2 62,6 62,6 62,6 Gesl1eVerz 1 1 1 1 1 1 Gesl2eVerz 2 2 2 2 2 2 Lft1eVerz 25,3 25,5 25,3 25,0 25,0 25,8 Input values Lft2eVerz 25,0 25,8 25,11 25,6 25,0 25,11 Values AF 21,98975 21,93225 21,98975 22,16700 22,16700 21,93900 Output

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input parameter ‘EindLftFinanc’ consists of years and months. When the input parameter ‘Lft1eVerz’ also consists of years and months the amount of faults will increase. Gesl1eVerz 1 1 1 1 1 1 RekenRente 3 3 3 3 3 3 Lft1eVerz 50,0 50,0 50,0 30,0 30,0 30,1 Input values EindLftFinanc 62,5 62,7 62,9 62,7 62,10 62,1 Values AF 10,22600 10,33300 10,44000 20,71100 20,79700 20,50575 Output

values of the input parameters are the following:

‘Gesl1eVerz’ = 2 ‘Lft1eVerz’ < 26,0 Months of ‘Lft1eVerz’ <> 0 Months of ‘EindLftUitk’ = 2 RekenRente 3 3 3 3 3 3 Gesl1eVerz 2 2 2 2 2 2 Gesl2eVerz 1 1 1 1 1 1 Lft1eVerz 20,1 20,6 21,5 21,7 24,9 25,11 Lft2eVerz 30,0 30,0 30,0 30,0 30,0 30,0 StijgRente 2 2 2 2 2 2 Input values EindLftUitk 75,2 75,2 75,2 75,2 75,2 75,2 Values AF 40,82850 40,58100 40,03483 39,93517 38,00825 37,28475 Output

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Function 100-3

The results generated by the ‘Actuarieel Formularium’ differs from the results generated by GAST. It is not clear what the cause of the differences is. In the table below some examples are given. Gesl1eVerz 1 1 1 2 2 2 RekenRente 3 3 3 3 3 3 Lft1eVerz 20,0 30,0 40,0 50,0 60,0 30,0 Input values EindLftFinanc 62,0 62,0 62,0 62,0 62,0 62,0 Values AF 23,54400 20,18200 15,68300 9,89400 1,93000 20,34000 Output