1 TEST SETS the general method data models extraction functional criterias data sets data sets before tests selection test execution extraction anonymisation / data generation analysis / validation data sets after testsexecution traces test coverage

2 FUNCTIONAL SUBSET derived from the model …and the usage graph SELECTION functional division The subset is the minimum list of necessary tables

3 VOLUMETRIC SUBSET rules definition for all the functional subset attributes SELECTION volumetric division

4 RULE TYPES 1. Simple criteria x = valeur 2. borders x <= <= x <= <= x 3. Addition or suppression of known records SELECTION volumetric division

5 extraction method F = first record S = 1 amongst N R = random number combination types U = union I = intersection X = exclusion processing sequence nbr of records to be extracted, per type Results Primary keys … SELECTION volumetric division

6 data models extraction functional criterias data sets data sets before tests selection test execution extraction anonymisation / data generation analysis / validation data sets after testsexecution traces test coverage TEST SETS the general method

7 EXTRACTION

8 Extraction combination coming from different DB ABCD EFGH … XYZT Schema 1 Extracteur DB … Key 1 Key 2 Schema 2 Rule 1 Attribute 1 = ‘X’ … Rule 2 Attribute 2 = ‘Y’ … DB 2 Extracteur Links element Data 1 Data 2 EXTRACTION

9 data models extraction functional criterias data sets data sets before tests selection test execution extraction anonymisation / data generation analysis / validation data sets after testsexecution traces test coverage TEST SETS the general method

10 ANONYMISATION

11 ListeRS.txt To define the data anonymisation rules The values of the attribute “RAISON SOCIAL” are read in the file "D:\Dgi\Database\ListeRS.txt" RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE RAISON SOCIALE … ANONYMISATION

12 ANONYMISATION

13 For each record “ SUPPORT JURIDIQUE” create randomly 1 to 3 record TIERS DATA GENERATION Generation rules

14 Contents generation DATA GENERATION

15 data models extraction functional criterias data sets data sets before tests selection test execution extraction anonymisation / data generation analysis / validation data sets after testsexecution traces test coverage TEST SETS the general method

16 Use of the extractors to obtain the data sets to be compared DB before tests … Extractor DB after tests ANALYSIS / VALIDATION

17 To define comparison criteria’s Looking for differences Some attributs might be different ANALYSIS / VALIDATION

18 <SUPPORTJURIDIQUE IDSJU = "53385" <TIERS IDTIERS = "85524" <DEFAILLANCE IDDEFAILLANCE="80307"/> > <TIERS IDTIERS = "85523" > <SUPPORTJURIDIQUE IDSJU = "53385" <TIERS IDTIERS = "85524" <DEFAILLANCE IDDEFAILLANCE="80307"/> > record TIERS was cancelled DB 1 extraction DB 2 extraction ANALYSIS / VALIDATION

19 VALUE DIFFERENCES Some value difference between attributes were ignored ex: MODIFICATIONDATE The path to the record is detailed The critical differences are detected ANALYSIS / VALIDATION

20 data models extraction functional criterias data sets data sets before tests selection test execution extraction anonymisation / data generation analysis / validation data sets after testsexecution traces test coverage TEST SETS the general method

21 PROGRAMS ARE AUTOMATICALLY INSTRUMENTED COVERAGE

22 RESULTS FROM THE ANALYSIS OF A PROGRAM TRACE FILE INCLUDING ARCS Arc numberNumber of processes Procedure name SR1CH1S SR1CH1S11 end …… … SR SR8 end ……… SR SR5 end ……… OPTI-EMPI OPTI-EMPI end The most used arcs The less used arcs COVERAGE