Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: processing a plurality of raw datasets for generating index metadata and associating one or more index defining the index metadata to respective ones of the plurality raw datasets, wherein the respective ones of the plurality of raw datasets define respective metadata augmented datasets by the associating of the one or more index thereto; examining augmented datasets of the metadata augmented datasets in dependence on metadata of the index metadata and in dependence on user defined input data; and merging first and second ones of the augmented datasets in dependence on the examining augmented datasets, wherein the merging first and second ones of the augmented datasets in dependence on the examining augmented datasets is performed in support of preparing a production dataset.

Abstract: A computer-implemented method, according to one embodiment, includes: identifying statements in a software program. Values that satisfy path constraints of a given one of the statements are developed for ones of the identified statements that semantics information is not yet known. Outputs are produced by executing the given one of the statements using the generated values as inputs. Moreover, semantics information corresponding to the given one of the statements is determined by evaluating the generated values and corresponding outputs using a machine learning model. The semantics information is further used to generate a symbolic representation of the given one of the statements.

Abstract: Automated testing of an application programming interface (API) includes determining features of an application programming interface (API). The features include operations of the API, one or more resources used by each operation, and resource-based dependencies between the operations. A resource-specific group of operations is generated. The operations within the resource-specific group operate on a selected resource selected from the one or more resources. A sequence of operations is generated by ordering the operations within the resource-specific group based on resource-based dependencies between the operations within the resource-specific group. A functional test case of the API based on the sequence of operations is output for testing the API.

Abstract: Retraining a model to present a target explanation with a prediction responsive to a source sample. The target explanation being selected from explanations provided by at least two machine learning models. A set of candidate samples is selected from samples generated from a relationship to the source sample. The retaining being performed with the set of candidate samples in a revised training dataset and causing a model presenting another explanation to present the target explanation with the prediction responsive to the source sample.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate testing a cascaded pipeline. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an input component, a cascaded pipeline, and an evaluation component. The input component can receive a test case associated with a label from labeled speech data represented by waveform. The evaluation component can feed the test case to the cascaded pipeline to obtain an output of the cascaded pipeline. The evaluation component can evaluate a robustness of the cascaded pipeline by comparing the output of the cascaded pipeline and the label. The cascaded pipeline can include a first model and a second model, and the first model can be different than the second model.

Abstract: Using encoded representations of target model training data and a label corresponding to each portion of the target model training data, a proxy model to determine an uncertainty score corresponding to an output of a trained target model is trained. Using the trained proxy model, a set of uncertainty scores is computed, each uncertainty score in the set of uncertainty scores corresponding to a portion of target model testing data in a set of target model testing data. A subset of the set of target model testing data is selected, the subset comprising a plurality of portions of target model testing data having an uncertainty score above a threshold uncertainty score. Using the subset of the set of target model testing data, the trained target model.

Abstract: A method of providing a surrogate program for a program endpoint includes: obtaining, by a processor set, a set of plural input/output pairs generated using the program endpoint; generating, by the processor set, transformations based on the input/output pairs; generating, by the processor set, a model that classifies inputs of the input/output pairs to ones of the transformations based on parameters of one or more strings of the inputs; receiving, by the processor set, a new input; selecting, by the processor set and using the model, one of the transformations based on parameters of one or more strings of the new input; and generating, by the processor set, a new output by applying the selected one of the transformations to the new input.

Abstract: One embodiment provides a method, including: providing, from the central server to a machine-learning model, a training set of samples having known values for at least one target protected attribute, wherein the training set includes a first set of samples having a first value for the at least one target protected attribute and a second set of samples having a second value for the at least one target protected attribute; receiving, at the central server from the machine-learning model, an output classification for each of the samples within the training set of samples; and generating, at the central server using the output classification, a set of rules delineating a region within the machine-learning model as discriminatory, wherein the region includes a classification region where the machine-learning model classifies received samples differently based upon a value of the at least one protected attribute.