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(i) The inherent complexity associated with the multiple possibilities in the biomaterials/cells selection, usually addressed using high-throughput combinatorial tests; and (ii) the unpredictability of the biological outcome of a particular solution.
These findings open the door for combinatorial tests of many different functional motifs in the future experiments.
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One of the approaches to reduce the number of test cases is Combinatorial Interaction Testing (CIT) (Petke et al. 2015), also known as Combinatorial Testing (CT) (Kuhn et al. 2013; Schroeder and Korel 2000), Combinatorial Test Design (CTD) (Tzoref-Brill et al. 2016), or Combinatorial Designs (CD) (Mathur 2008).
Pairwise indepedent combinatorial testing.
Advanced combinatorial test system.
In addition to this, all of them do not address mutation and combinatorial testing at the same time.
The Feedback Driven Adptative Combinatorial Testing Process (FDA-CIT) algorithm is shown in (Yilmaz et al. 2014).
This chapter reviews recent advances in combinatorial testing, with special focus on the research since 2011.
The experimental requirements for a valid combinatorial test are analyzed using three-dimensional computational fracture mechanics.
We discuss the development of combinatorial testing for software as adaptation of design of experiment methods.
We compare a specification-based approach to three non-specification-based approaches, namely, random testing, antirandom testing, and combinatorial testing.
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