Abstract: At a machine learning service, a data structure generated during the training phase of a machine learning model, as well as an input records associated with a result of the model, are analyzed. A first informational data set pertaining to the result, which indicates an alternative result, is generated. The first informational data set is transmitted to a presentation device with a directive to display a visual representation of the data set. In response to an exploration request pertaining to the first informational data set, a second informational data set indicating one or more observations of a training data set used for the model is transmitted to the presentation device.

Abstract: Methods, systems, and computer-readable media for taxonomy generation with automated analysis and auditing are disclosed. A primary classification is determined for a hierarchical taxonomy of items in a marketplace. The primary classification is selected from a plurality of terms describing items in the marketplace, and the primary classification is selected based at least in part on automated analysis of the terms. A plurality of secondary classifications are determined for the hierarchical taxonomy. The secondary classifications are selected from the terms describing the items in the marketplace, and the secondary classifications are selected based at least in part on automated analysis of the terms. The hierarchical taxonomy is modified based at least in part on feedback from a plurality of users. The feedback comprises one or more terms entered by one or more of the users to filter a set of items.

Abstract: A technology for determining accuracy estimates for classifications used in an electronic catalog. In one example, classifications for product groupings included in an electronic catalog may be updated as a result of the classifications inaccurately representing products included in the product groupings. The electronic catalog of products may be grouped into a plurality of product groupings using classifications. Classifications of product groupings that inaccurately represent products included in the product grouping may be updated with suggested classifications. Update metrics for updates made to the grouping classifications may be collected and the update metrics may be used to calculate an accuracy estimate for the classifications used in the electronic catalog.

Abstract: Techniques are provided herein for utilizing a classification engine to improve a classification model. For example, a classification engine may derive a statistical model based at least in part on a synthetic data set. A misclassification may be determined based at least in part on an output of the statistical model. An audit question may be provided to an individual, the audit question being determined based at least in part on the determined misclassification. Response data related to the audit question may be received. The statistical model may be validated based at least in part on the response data.

Abstract: One or more process models from natural language use case models are derived, for example, by creating, using a processor, an in-memory model of a use case from information in natural language text describing the use case; transforming the in-memory model into a process model in predetermined modeling notation; and generating a selected business process model using the process model.

Abstract: An automated system and method to generate functional conformance tests for applications are provided. The system and method in one aspect use Inputs, Outputs, Preconditions, Effects (IOPEs) paradigm associated with an application for automatically generating test goals. A planner component may accept these testing goals to generate a sequence of operations or invocations as a test case. Verification sequences are also generated. The system and method also allow generation of executable test cases, which can be applied to the various interfaces through which the application may be accessed.

Abstract: A method and system of reducing risk in the life cycle of a product, in one aspect, obtain one or more tasks required to achieve an overall task, determine risk impact of each task, the risk impact being an impact of each task on the likelihood of failure for the overall task and compute risk of overall task based on risk impacts of the tasks. The method and system utilize said risk impact of each task to schedule the tasks in such a way so as to reduce said risk of overall task as rapidly as possible.

Abstract: One or more process models from natural language use case models are derived, for example, by creating, using a processor, an in-memory model of a use case from information in natural language text describing the use case; transforming the in-memory model into a process model in predetermined modeling notation; and generating a selected business process model using the process model.

Abstract: A system and method for identifying modeling errors in textual use case description analyze an input text describing a use case and create an application model representing the use case, the application model containing information obtained from analyzing the input text describing the use case. The application model may be automatically analyzed using automatic process and one or more errors in the use case and/or reports about the use case may be generated. In one aspect, processing components may be integrated into a user development environment to allow developing use cases and improving them incrementally and/or iteratively as information is identified about the use cases.

Abstract: Techniques are provided for calculating effort of a software application. The techniques include obtaining a detailed use case model (DUCM) of the software application, computing a multi-dimensional metrics vector (MMV) based on the DUCM, using a size model for the MMV to estimate a size of the software application, and inputting the MMV and the size into an effort model, wherein the effort model is used to calculate the effort required to build the software application. Techniques are also provided for representing a DUCM of a software application.

Abstract: A method and system of reducing risk in the life cycle of a product, in one aspect, obtain one or more tasks required to achieve an overall task, determine risk impact of each task, the risk impact being an impact of each task on the likelihood of failure for the overall task and compute risk of overall task based on risk impacts of the tasks. The method and system utilize said risk impact of each task to schedule the tasks in such a way so as to reduce said risk of overall task as rapidly as possible.

Abstract: An automated system and method to generate functional conformance tests for applications are provided. The system and method in one aspect use Inputs, Outputs, Preconditions, Effects (IOPEs) paradigm associated with an application for automatically generating test goals. A planner component may accept these testing goals to generate a sequence of operations or invocations as a test case. Verification sequences are also generated. The system and method also allow generation of executable test cases, which can be applied to the various interfaces through which the application may be accessed.

Abstract: A method for automatically generating test cases from a domain specific description language specification makes use of the properties of the language to derive domain specific axioms and language specific predicates. These properties are embedded into an extended finite state machine which is in turn supplied to the input of a test case generator. The enhanced extended finite state machine, referred herein as an extended finite state machine accounting for axioms and predicates (EFSMAP) contains states and transitions associated with information on implied behavior of the specified system within a particular problem domain. The implicit behavior, defined by the axiomatic properties of the operators of the domain specific language, provide test capability of the associated system that was not explicitly set forth in the formal specification, but nevertheless should be tested to increase confidence in the reliability of the finished product.

Abstract: A method for automatically generating test cases from a domain specific description language specification makes use of the properties of the language to derive domain specific axioms and language specific predicates. These properties are embedded into an extended finite state machine which is in turn supplied to the input of a test case generator. The enhanced extended finite state machine, referred herein as an extended finite state machine accounting for axioms and predicates (EFSMAP) contains states and transitions associated with information on implied behavior of the specified system within a particular problem domain. The implicit behavior, defined by the axiomatic properties of the operators of the domain specific language, provide test capability of the associated system that was not explicitly set forth in the formal specification, but nevertheless should be tested to increase confidence in the reliability of the finished product.