Combinatorial interaction testing (CIT) is a testing technique that has proved to be effective in finding faults due to the interaction among inputs, and in reducing the number of test cases. One of the most crucial parts of combinatorial testing is the test generation for which many tools and algorithms have been proposed in recent years, with different methodologies and performances. However, generating tests remains a complex procedure that can require a lot of effort (mainly time). Thus, in this paper, we present the tool pMEDICI which aims to reduce the test generation time by parallelizing the generation process and exploiting the recent multithread hardware architectures. It uses Multivalued Decision Diagrams (MDDs) for representing the constraints and the tuples to be tested and extracts from them the t-wise test cases. Our experiments confirm that our tool requires a shorter amount of time for generating combinatorial test suites, especially for complex models, with a lot of parameters and constraints.

(2022). Parallel Test Generation for Combinatorial Models Based on Multivalued Decision Diagrams . Retrieved from https://hdl.handle.net/10446/235449

Parallel Test Generation for Combinatorial Models Based on Multivalued Decision Diagrams

Bombarda, Andrea;Gargantini, Angelo
2022-01-01

Abstract

Combinatorial interaction testing (CIT) is a testing technique that has proved to be effective in finding faults due to the interaction among inputs, and in reducing the number of test cases. One of the most crucial parts of combinatorial testing is the test generation for which many tools and algorithms have been proposed in recent years, with different methodologies and performances. However, generating tests remains a complex procedure that can require a lot of effort (mainly time). Thus, in this paper, we present the tool pMEDICI which aims to reduce the test generation time by parallelizing the generation process and exploiting the recent multithread hardware architectures. It uses Multivalued Decision Diagrams (MDDs) for representing the constraints and the tuples to be tested and extracts from them the t-wise test cases. Our experiments confirm that our tool requires a shorter amount of time for generating combinatorial test suites, especially for complex models, with a lot of parameters and constraints.
2022
Bombarda, Andrea; Gargantini, Angelo Michele
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/235449
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