test data generation for covering functionality of database applications
DESCRIPTION
Test data generation for covering functionality of database applications . Authors: Sergey Zelenov, Vitaly Omelchenko, Evgeny Kostychev Institute for System Programming at the Russian Academy of Sciences Moscow, Russian Federation . Target. Source. Extract. Load. Transform. - PowerPoint PPT PresentationTRANSCRIPT
Test data generation for covering functionality of database applications
Authors: Sergey Zelenov, Vitaly Omelchenko, Evgeny Kostychev
Institute for System Programming at the Russian Academy of Sciences
Moscow, Russian Federation
Database - applications
• extraction source data• changing values of source data• transformation of input data• loading data • changing in the target some data
Extract
Change
TransformLoad
Change
Source Target
Problem
Cartesian product
combinatorial explosion
problem of analyzing test results
Goal
• cover all branches of the functionality• test set → min
Ideally• one test data set per one functionality branch
Hierarchy of combinators
field1 field2 field3 field4
Cartesian product
Cartesian product
All X with any YAll Y with any X
X Y
Comparison of the toolsDTM Data Generator
Turbo Data
DB Monster
Agenda HTDGen Pinery
generation for one field
random
from tables
from libraries
auto incremental
random by a mask
dependent tables
user procedures
generation for many fields
combinators
hierarchy of different combinators
Method description
Model ofthe SUT
Catalogue ofrequirements
Documentation analysis
Formalization of
requirements
Formulation coveragecriteria
Normativedocuments
Generationof test data
CRITERIA
- …- …- …- …
Coveragecriteria
Testset
ExampleBank credit system
• the client type (V, U)• the credit type (A, B) bonus or penalty• the repaid sum
Model ofthe SUT
Catalogue ofrequirements
Documentation analysis
Formalization of
requirements
Formulation coveragecriteria
Normativedocuments
Generationof test data
CRITERIA
- …- …- …- …
Coveragecriteria
Testset
1. Requirements elicitation
Monthly ≤ Repay ≤ Debt
VIP USUAL
Repay < Monthly
USUAL VIP
Repay > Debt
Loan ‘A’ Loan ‘B’ Loan ‘A’ Loan ‘B’
A
Penalty0
Penalty3%
Bonus 2%
Bonus 5%
Bonus 1%
Refund
B
e-mail is specified
Notification
Model ofthe SUT
Catalogue ofrequirements
Documentation analysis
Formalization of
requirements
Formulation coveragecriteria
Normativedocuments
Generationof test data
CRITERIA
- …- …- …- …
Coveragecriteria
Testset
MP ≤ P ≤ D
“V” “U”
P < MP
“U” “V”
P > D
“A” “B” “A” “B”
A
0 -3% +2%+5% +1% refund
B
Notification
MP to be pay
D debt
P repaid
CL client type (V, U)
CR credit type (A, B)
E e-mail
A.I P<MP && CL="V" =>penalty 0;
P<MP && CL="U" =>penalty 3%;
A.II.1 MP≤P≤D && (CL="V" && CR="B" || CL="U" && CR="A")
=> bonus 2%;
A.II.2 MP≤P≤D && CL="V" && CR="A" => bonus 5%;
A.II.3 MP≤P≤D && CL="U" && CR="B" => bonus 1%;
A.III P>D => refund.
B. E≠"" => notification.
2. Formalization of requirements
Model ofthe SUT
Catalogue ofrequirements
Documentation analysis
Formalization of
requirements
Formulation coveragecriteria
Normativedocuments
Generationof test data
CRITERIA
- …- …- …- …
Coveragecriteria
Testset
3. Formulation of coverage criteria The division rules for parameter P
•MP = D
•MP < DMP=D
P<MP P>MPP=MP
MP<P<DP<MP P>D
P=MP P=D
The division rules for parameters CL and CR
•P < MP → 2 subsets (CL)
•MP ≤ P ≤ D → 4 subsets (CL × CR)
•P > D → 1 set MP ≤ P ≤ D
“V” “U”
P < MP
“U” “V”
P > D
“A” “B” “A” “B”
A
0 -3% +2%+5% +1% refund
The division rules for the parameter E
•2 subsets (empty, non-empty)
B
Notification
Independent aspects
MP ≤ P ≤ D
“V” “U”
P < MP
“U” “V”
P > D
“A” “B” “A” “B”
A
0 -3% +2%+5% +1% 0
B
Notification
Model ofthe SUT
Catalogue ofrequirements
Documentation analysis
Formalization of
requirements
Formulation coveragecriteria
Normativedocuments
Generationof test data
CRITERIA
- …- …- …- …
Coveragecriteria
Testset
4. Generation of test data
Description ofDB scheme
Configuration
PineryTest set
Test setTest set
Test set
Configuration of Pinery•Constraints on values of one field
– MP = { 6 }; – D = { 6, 30 }; – E = { "", "…@…" }; – P[MP<D] = {MP-1, MP, (MP+D)/2, D, D+1};– P[MP=D] = {MP - 1, D, D + 1};– CL[ P<MP ] = { "V", "U" };– CR[ P<MP ] = { "A" };– ….
• Constraints on combination method for several fields– combinator( CREDITS ) =
Diagonal( Depend( Depend( Product(MP, D), P), Product(CL, CR) ),
E );
MP ≤ P ≤ D
“V” “U”
P < MP
“U” “V”
P > D
“A” “B” “A” “B”
A
0 -3% +2%+5% +1% 0
B
Notification
Results
• Our method: 22 tuples• Cartesian product: 64 tuples
Economy increases if• possible values of fields ↗• number of independent aspects ↗
Thank you !