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Test data generation for covering functionality of database applications

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.

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Test data generation for covering functionality of database applications

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  1. 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

  2. Target Source Extract Load Transform Database - applications Change Change extraction source data changing values of source data transformation of input data loading data changing in the target some data

  3. Problem Cartesian product combinatorial explosion problem of analyzing test results

  4. Goal • cover all branches of the functionality • test set → min Ideally • one test data set per one functionality branch

  5. Hierarchy of combinators X Y field1 field2 field3 field4 Cartesian product Cartesian product All X with any Y All Y with any X

  6. Comparison of the tools

  7. Documentation analysis Formalization of requirements Formulation coverage criteria Generation of test data Normative documents Catalogue of requirements Coverage criteria Model of the SUT Test set CRITERIA - … - … - … - … Method description

  8. ExampleBank credit system • the client type (V, U) • the credit type (A, B) bonus or penalty • the repaid sum

  9. Documentation analysis Formalization of requirements Formulation coverage criteria Generation of test data Normative documents Catalogue of requirements Coverage criteria Model of the SUT Test set CRITERIA - … - … - … - …

  10. A 1. Requirements elicitation Repay< Monthly Repay > Debt Monthly  ≤  Repay  ≤  Debt VIP USUAL VIP USUAL Loan‘B’ Loan‘A’ Loan‘B’ Loan‘A’ Penalty0 Penalty3% Bonus 5% Bonus 2% Bonus 1% Refund B e-mail is specified Notification

  11. Documentation analysis Formalization of requirements Formulation coverage criteria Generation of test data Normative documents Catalogue of requirements Coverage criteria Model of the SUT Test set CRITERIA - … - … - … - …

  12. A 2. Formalization of requirements P< MP P > D MP  ≤  P  ≤  D “V” “U” “U” “V” “B” “B” “A” “A” 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. refund -3% +5% 0 +2% +1% B e-mail Notification

  13. Documentation analysis Formalization of requirements Formulation coverage criteria Generation of test data Normative documents Catalogue of requirements Coverage criteria Model of the SUT Test set CRITERIA - … - … - … - …

  14. 3. Formulation of coverage criteria The division rules for parameter P • MP = D • MP < D P=MP P>MP P<MP P=D P=MP MP=D P>D P<MP MP<P<D

  15. A P< MP P > D 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 “V” “U” “U” “V” “B” “B” “A” “A” refund -3% +5% 0 +2% +1%

  16. The division rules for the parameter E • 2 subsets (empty, non-empty) B e-mail Notification

  17. A P< MP P > D MP  ≤  P  ≤  D Independent aspects “V” “U” “U” “V” “B” “B” “A” “A” 0 -3% +5% 0 +2% +1% B e-mail Notification

  18. Documentation analysis Formalization of requirements Formulation coverage criteria Generation of test data Normative documents Catalogue of requirements Coverage criteria Model of the SUT Test set CRITERIA - … - … - … - …

  19. 4. Generation of test data Description of DB scheme Pinery Test set Test set Test set Test set Configuration

  20. 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" }; • ….

  21. A P< MP P > D MP  ≤  P  ≤  D “V” “U” “U” “V” “B” “B” “A” “A” 0 -3% +5% 0 +2% +1% • Constraints on combination method for several fields • combinator( CREDITS ) = Diagonal( Depend( Depend( Product(MP, D), P), Product(CL, CR) ), E ); B e-mail Notification

  22. Results • Our method: 22 tuples • Cartesian product: 64 tuples Economy increases if • possible values of fields ↗ • number of independent aspects ↗

  23. Thank you !

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