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1. WO2020160810 - GÉNÉRATION DE MODÈLES DE TEST À PARTIR DE SCÉNARIOS DE DÉVELOPPEMENT DICTÉ PAR LE COMPORTEMENT SUR LA BASE DE DÉFINITIONS D'ÉTAPES DE DÉVELOPPEMENT DICTÉ PAR LE COMPORTEMENT ET D'UNE ANALYSE DE SIMILARITÉ À L'AIDE DE MÉCANISMES DE PROGRAMMATION NEURO-LINGUISTIQUE ET D'APPRENTISSAGE AUTOMATIQUE

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

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Patent Claims

1. A computer-implemented method (M) for automated verific-tion of a software program in a behavior-driven development environment, the method (M) comprising:

receiving (M0), with a data processing system (10), test scenarios (1), each test scenario (1) defining an ex pected behavior of the software program in consecutive test steps (2), which are formulated in a domain- specific language using natural language phrases and which describe a desired outcome of the software program for predefined events based on given initial conditions; importing (Ml) test step definitions (3) from the behav ior-driven development environment;

determining (M2) for each test step (2) of the test sce narios (1) if the test step (2) matches with one of the test step definitions (3) on basis of the natural lan guage phrases of the test step (2);

assigning (M3) all matched test steps (2) to the corre sponding test step definitions (3) ;

applying (M4) natural language processing, NLP, on the natural language phrases of any test steps (2) remaining unmatched, wherein the NLP provides a confidence level for each unmatched test step (2) to correspond to one of the test step definitions (3) ;

assigning (M5) any unmatched test step (2) to the corre sponding test step definition (3) when the confidence level surpasses a first predefined matching probability; and at least one of:

generating (M6) graphical test models (4) for the test scenarios (1) on basis of the assigned test step defini tions (3) ; and

generating (M7) executable test scripts (6) for the test scenarios (1) on basis of the assigned test step defini tions (3) .

2. The method (M) according to claim 1, further comprising: updating (Tl), when the confidence level is above the first predefined matching probability, the respective test step definition (3) on basis of the natural word phrases of the respective test step (2) .

3. The method (M) according to claim 1 or 2, further compris ing :

adding (T2), when the confidence level is below a second predefined matching probability, a test step definition (3) to the behavior-driven development environment cor responding to the respective test step (2) .

4. The method (M) according to one of the claims 1 to 3, wherein a user verification is requested if the confidence level is below the first predefined matching probability but above a second predefined matching probability.

5. The method (M) according to claim 4, further comprising: feeding (T3) the user verification to a machine learning al gorithm of the NLP.

6. The method (M) according to one of the claims 1 to 5, wherein generating the graphical test models (4) comprises combining similar test scenarios (1) on basis of test steps (2) assigned to the same test step definition (3) .

7. The method (M) according to one of the claims 1 to 6, wherein generating the graphical test models (4) comprises identifying test data (5) within the test scenarios (1) based on the natural language phrases.

8. The method (M) according to one of the claims 1 to 7, wherein the graphical test models (4) comprise unified model ing language diagrams.

9. The method (M) according to one of the claims 1 to 8, fur ther comprising:

comparing (T4) the graphical test models (4) with the test scenarios (1) to determine if the graphical test models (4) are in compliance with the expected behavior of the software program.

10. A data processing system (10) comprising a processor (11) configured to perform a method (M) according to any of claims 1 to 9.

11. A computer program product comprising executable program instructions configured to, when executed, perform the method (M) according to any of claims 1 to 9.

12. A non-transient computer-readable data storage medium comprising executable program instructions configured to, when executed, perform the method (M) according to any of claims 1 to 9.