Abstract: A computing system generates a subset of design cases of candidate design cases. The system indexes, in the subset, data elements. The system generates a design of an experiment by, for each respective data element, determining a status indicating whether the respective data element corresponds to an uncontrolled factor or a controlled factor. When the status indicates the uncontrolled factor, the system determines if substituting a respective set of specified options of a respective candidate design case comprising the respective data element with a different set of specified options of the candidate design cases improves a criterion measure according to a design criterion. When the status indicates the controlled factor, the system determines if changing an assigned option of the respective data element improves the criterion measure. The system updates the criterion measure with an updated criterion measure according to a change of the subset based on generating the design.

Abstract: A computing system receives a request to validate, according to a validation specification, a response of a system of operation. The validation specification relates to determining deviation from a specified result in response to testing one or more candidate inputs. The initial design space defines design points for the system of operation according to the respective candidate inputs for each factor. The computing system, responsive to the request to validate the response of the system of operation, generates data representing a covering array for design points defined by the initial design space or a subset design space. The computing system generates, based on the data, a test suite for testing the system of operation. The computing system obtains the response of the system of operation and generates an output indicating the deviation from the specified result.

Abstract: A computing system determines a design space for designing a design system. The computing system receives a request to identify input(s) that will provide a response of the design system that advances a user-defined goal for the design system. The computing system, responsive to the request to identify input(s), generates a design for the design space that provides design cases for generating the response of the design system and obtains responses of the design system generated for multiple design cases of the design for the design system. The computing system selects at least one design case based on obtained responses of the design system for the multiple design cases, and based on the at least one design case, outputs an indication of suggested input(s) for the design system. The suggested input(s) advances the user-defined goal for the design system and is not disallowed according to disallowed combination(s).

Abstract: A computing system receives a request for a computer-generated design of an experiment. A design space is defined by candidate inputs for each factor of the experiment. The system receives a disallowed combination indication indicating a user-defined disallowed combination for the design space. The disallowed combination constrains a first set of values from a first set of candidate inputs from being assigned to the first factor if the second factor is assigned one of a second set of values from a second set of candidate inputs. The system determines additional constraint(s) on computer generation of the design. The system evaluates if a user-defined model can be generated according to the disallowed combination and the constraint(s). The system generates a computer suggested modification of the user-defined model such that the design can be generated according to the computer suggested modification.

Abstract: A computing system receives a request to validate, according to a validation specification, a response of a system of operation. The validation specification relates to determining deviation from a specified result in response to testing one or more candidate inputs. The initial design space defines design points for the system of operation according to the respective candidate inputs for each factor. The computing system, responsive to the request to validate the response of the system of operation, generates data representing a covering array for design points defined by the initial design space or a subset design space. The computing system generates, based on the data, a test suite for testing the system of operation. The computing system obtains the response of the system of operation and generates an output indicating the deviation from the specified result.

Abstract: A computing device receives a request for a design of an experiment. The device generates a data representation of a matrix Y that defines a supersaturated design for the design of the experiment. The generating the data representation is by: generating a data representation of a matrix X according to an obtained design; computing an indication of correlation between effects of factors of a matrix Y; and generating, based on the indication of correlation, the data representation of the matrix Y that is the transposition of the matrix X or is the transposition of a matrix X*. The matrix X* is a first subset of the matrix X such that the transposition of the matrix X* represents a same number of factors as the transposition of the matrix X. The device outputs a setting for each test condition of the supersaturated design for the experiment.

Abstract: A computing device receives a request for a design of an experiment. The design comprises test cases with test conditions for testing factors for the experiment. The device receives a value for a parameter of multiple parameters for the design. The multiple parameters indicate a total number of test cases for the design, a total number of factors for the design, and a total number of groups for grouping factors. The device generates a value for each of one or more other parameters of the multiple parameters such that the design is a supersaturated design. The device generates, based on the first value for the first parameter and the value for each of the one or more other parameters, the supersaturated design for the experiment that is a design that distributes each of the factors into one of the groups. The device outputs an indication of the supersaturated design.

Abstract: A computing device obtains a metric N indicating a quantity of a plurality of test cases for an output design of an experiment Each element of a test case of the output design is a test condition for testing one of factors for the experiment. The computing device obtains input indicating a quantity p of an indicated plurality of factors for the output design. The computing device determines whether there are stored instructions for generating an initial screening design for the experiment. The computing device responsive to determining that there are stored instructions, selects, using the stored instructions, the initial screening design for the experiment. The computing device determines whether to modify the initial screening design based on modification criteria comprising a secondary criterion, the metric N, and/or the quantity p. The computing device outputs an indication of the updated screening design for the output design of the experiment.

Abstract: A computing device obtains a metric N indicating a quantity of a plurality of test cases for an output design of an experiment. Each element of a test case of the output design is a test condition for testing one of factors for the experiment. The computing device obtains input indicating a quantity p of an indicated plurality of factors for the output design. The computing device determines whether there are stored instructions for generating an initial screening design for the experiment. The computing device responsive to determining that there are stored instructions, selects, using the stored instructions, the initial screening design for the experiment. The computing device determines whether to modify the initial screening design based on modification criteria comprising a secondary criterion, the metric N, and/or the quantity p. The computing device outputs an indication of the updated screening design for the output design of the experiment.

Abstract: The computing device receives information representing a design of an experiment. The design of the experiment comprises a plurality of test cases. Each element of a test case of the design is a test condition for testing one of factors for the experiment. The computing device generates a graphical representation based on the information. The graphical representation comprises a plurality of axes for respective ones of factors used in a fractal sequence. Each of the plurality of axes has two endpoints comprising a first endpoint that corresponds to a first level of a respective factor, and a second endpoint that corresponds to a second level of the respective factor. The computing device plots, on the graphical representation, data corresponding to one or more test cases of the plurality of test cases. The computing device displays an interactive graphical user interface comprising the graphical representation with the plotted data.