Abstract: A computer-implemented system, computer-implemented method, and computer-program product includes receiving, via a graphical user interface, a request to generate an initial design of experiment plan based on one or more experiment factors; generating a first plan settings container object that represents the initial design of experiment plan in computer memory; and instantiating, within the graphical user interface, a plan explorer section that includes a plan settings graphical entry that displays a graphical representation of a plan factors data object and a plan model data object associated with the first plan settings container object.

Abstract: A computer-implemented system, computer-implemented method, and computer-program product includes receiving, via a graphical user interface, a request to generate an initial design of experiment plan based on one or more experiment factors; generating a first plan settings container object that represents the initial design of experiment plan in computer memory; and instantiating, within the graphical user interface, a plan explorer section that includes a plan settings graphical entry that displays a graphical representation of a plan factors data object and a plan model data object associated with the first plan settings container object.

Abstract: A computer-implemented system, computer-implemented method, and computer-program product includes receiving, via a graphical user interface, a request to generate an initial design of experiment plan based on one or more experiment factors; generating a first plan settings container object that represents the initial design of experiment plan in computer memory; and instantiating, within the graphical user interface, a plan explorer section that includes a plan settings graphical entry that displays a graphical representation of a plan factors data object and a plan model data object associated with the first plan settings container object.

Abstract: A system, method, and computer-program product includes obtaining, via a graphical user interface: an input of a measured unit distribution derived from measurements, by a measuring device, of a plurality of instances of a physical unit; an input of characteristics of the measuring device used in the measurements of the plurality of instances of the physical unit; and an input of a type of distribution for fitting a set of measurement values of the physical unit to a target distribution; computing, via a unit distribution estimation algorithm, an estimated true unit distribution of the plurality of instances of the physical unit based on (a) the input of the measured unit distribution, (b) the input of the characteristics of the measuring device, and (c) the input of the type of distribution; and using quantitative characteristics of the estimated true unit distribution to mitigate binning classification error of the physical units.

Abstract: A system, method, and computer-program product includes obtaining application configuration data; computing, via an execution of a designed experiment, a plurality of application usability tests based on the application configuration data; generating a computer-executable application based at least on the plurality of application usability tests; and deploying the computer-executable application to a target computing environment for execution by one or more users.

Abstract: A computing device receives a request requesting a screening design for an experiment. The device obtains factors for screening. A first factor has a first set of candidate inputs comprising at least two candidate inputs for allowable inputs in the experiment. A second factor has a second set of candidate inputs comprising at least three candidate inputs for allowable inputs in the experiment. The device receives an indication of a run size. The device generates the screening design by assigning, based on the run size, the first factor to a two-level group that only allows at most two allowable inputs for the first factor in the experiment. The device generates the screening design by assigning, based on the initial run size, the second factor to a three-level group that only allows at most three allowable inputs for the second factor in the experiment. The device outputs the screening design.

Abstract: A computing device receives a request requesting a screening design for an experiment. The device obtains, for the screening design, factors for screening in the experiment. A first factor is a categorical factor with a first set of candidate inputs comprising at least three candidate inputs in the experiment. A second factor is a continuous factor with a second set of candidate inputs comprising candidate input options within range(s) for the second factor. The device receives an indication of a run size that is a user-requested quantity of runs for the screening design. The device generates the screening design based on the run size. The device outputs the screening design. The screening design comprises runs of a quantity based on the user-requested quantity of runs for the screening design. The runs comprise the first set of candidate inputs and at least three inputs of the second set of candidate inputs.

Abstract: A computing device receives a target value for a design quality metric. The target value indicates a desired quality of a design of an experiment and is related to an input parameter by a response curve. The computing device also validates the target value as being in a feasibility range for the design quality metric, and if so, determines a candidate value for the input parameter that yields a calculated value for the design quality metric. To determine the calculated value, the computing device iteratively adjusts the candidate value until the calculated value is within a predetermined tolerance of the target value. The computing device then updates an interactive graph visually representing the calculated value for the design quality metric as a function of the candidate value for the input parameter and outputs a graph visually representing the relationship that exists between the candidate value and the calculated value.

Abstract: A computing device obtains settings for a design of an experiment. The settings include a first sample size that indicates an amount of members for a first group observed as part of the experiment, a design quality metric that assesses, based on the first sample size, a quality of the design for the experiment prior to conducting the experiment, and a parameter pertaining to the first group. The computing device also displays first and second graphs of the design quality metric on a graphical user interface. The first graph is displayed as a function of the first sample size and the second graph is displayed as a function of the parameter pertaining to the first group. The computing device also receives a first user indication to change one or more of the settings, and then updates both the first and second graphs to account for the first user indication.

Abstract: An apparatus includes a processor to: generate variants of an experiment design based on varied parameters; for each variant, estimate terms based on the model, and derive an optimality value; present a table of the variants including a column for each varied parameter and a column for the optimality value, a row for each variant, and a bar graph for each column depicting a distribution of the values therein; present function controls operable to select a function to perform on row(s) of the table in response to selection of a bar of a bar graph of a column; in response to selection of a function, change the current function to the selected function; and in response to a selection of a bar of a bar graph of a column, perform the current function on row(s) based on instances of the value associated with selected bar.

Abstract: An apparatus includes a processor to: present prompts guiding selection of first subset terms for estimation of variants of an experiment design based on a model thereof, and second subset terms for estimation using Bayesian modification; monitor for selections of term; in response to each change in the first subset terms, determine whether all first subset terms can be estimated based on the model; in response to the first subset including too many terms, present an indication of too many terms being selected for estimation based on the model; in response to the first subset including fewer terms than can be estimated based on the model, present an indication that more terms could be selected for estimation based on the model; and for each variant of the experiment design, estimate each first subset term based on the model, and estimate each second subset term using Bayesian modification.

Abstract: A computing system obtains computer model(s) configured to predict response(s) based on variable(s). The system obtains a specification defining an allowed response set for the response(s) and receives an initial setting for bound(s). The system generates an initial design space for the variable(s) defined by the initial setting. The system displays in a graphical user interface (GUI) an initial representation of a specification rate. The specification rate indicates a portion of the initial design space predicted to generate a response within the allowed response set defined by the specification. The system receives an updated setting. The system generates an updated design space for the variable(s) defined by the updated setting. The system displays in the GUI an updated representation of an updated specification rate. The updated specification rate indicates a portion of the updated design space predicted to generate a response within the allowed response set defined by the specification.

Abstract: An apparatus includes a processor to: present prompts guiding selection of first subset terms for estimation of variants of an experiment design based on a model thereof, and second subset terms for estimation using Bayesian modification; monitor for selections of term; in response to each change in the first subset terms, determine whether all first subset terms can be estimated based on the model; in response to the first subset including too many terms, present an indication of too many terms being selected for estimation based on the model; in response to the first subset including fewer terms than can be estimated based on the model, present an indication that more terms could be selected for estimation based on the model; and for each variant of the experiment design, estimate each first subset term based on the model, and estimate each second subset term using Bayesian modification.

Abstract: A computing device receives a request for a design of a combinatorial test for a test system. The device receives a run indication of a total quantity of test cases for the design, a factor indication of a total quantity of factors, and/or a strength indication for a covering array. The device generates an updated design by: selecting test case(s) to remove from a first design; or adding test case(s) to the first design. The first design comprises a set of test cases that represent the covering array according to the strength indication. The updated design is constrained to the total quantity of test cases as indicated by the run indication. The device outputs a respective setting for each test condition for at least one test case of the updated design for testing the test system.