Abstract: Implementations include providing a robotic model representative of a robotic system, determining baseline data by executing a simulation using the robotic model and a simulation world that represents the environment, generating, by a perturbation system, perturbations to be included in a set of test cases, each test case including at least one perturbation to at least one object relative to a baseline of the at least one object determined from the baseline data, determining test result data for the set of test cases by modifying the at least one object in view of the at least one perturbation to provide a modified object and executing the simulation with the modified object, and displaying a graph representative of the at least one object based on the baseline data and test result data for a test case, the graph depicting variance of the object during the simulation using the test case.

Abstract: Multi-data analysis based proactive defect detection and resolution may include analyzing operational data for an application to determine whether a functionality related to the application is below a predetermined threshold associated with the functionality related to the application, and based on the analysis, generating an indication to perform defect analysis related to the functionality related to the application. A sentiment analysis may be performed on consumer data related to the application to determine a sentiment of the consumer data related to the application, and a natural language processing (NLP) analysis may be performed on the consumer data related to the application to determine a function associated with a negative sentiment. Application code and process data related to the application may be analyzed to determine a defect associated with the application. Further, a code of the application may be modified to correct the defect associated with the application.

Abstract: According to an example, source code flow analysis may include receiving source code for an application, and identifying virtual flow documents for the application from the source code. The virtual flow documents may represent ordered sequences of method calls for the application. The source code flow analysis may further include extracting features of the virtual flow documents, determining similarity between the virtual flow documents by estimating similarities for the extracted features to determine a flow-to-flow similarity, and clustering the virtual flow documents based on the flow-to-flow similarity. The flow-to-flow similarity may be further used, for example, to generate highest priority virtual flow documents and methods for the source code.

Abstract: Workflow test case generation may include receiving an intermediate output of a workflow. The intermediate output of the workflow may reference a plurality of workflow building blocks that provide a specified functionality at each step of the workflow. Workflow definitions of the intermediate output of the workflow may be analyzed to generate a graph structure that represents the workflow. Building block attributes that include an identification of attributes for the plurality of workflow building blocks, and the graph structure may be analyzed to generate building block and attribute information for the workflow. The building block and attribute information and the graph structure may be analyzed to generate test paths for the workflow. The building block and attribute information, the test paths, and a plurality of test case templates may be analyzed to generate test artifacts that include testing to be performed for the workflow.

Abstract: A plurality of coding violation indications corresponding to software code are categorized according to a plurality of coding quality attributes. The coding violation indications may be reported via static analysis tools or via manual analysis of the software code. The categorized coding violations serve as the basis for determining a plurality of code quality indices each corresponding to a respective one of the plurality of coding quality attributes. In an embodiment, the plurality of code quality indices may be further processed to determine an overall code quality index for the software code. One or more of the resulting code quality indices may be displayed to a user. In this manner, code quality indices may be provided that are directly based on coding violations reported through static analysis.

Abstract: Multi-data analysis based proactive defect detection and resolution may include analyzing operational data for an application to determine whether a functionality related to the application is below a predetermined threshold associated with the functionality related to the application, and based on the analysis, generating an indication to perform defect analysis related to the functionality related to the application. A sentiment analysis may be performed on consumer data related to the application to determine a sentiment of the consumer data related to the application, and a natural language processing (NLP) analysis may be performed on the consumer data related to the application to determine a function associated with a negative sentiment. Application code and process data related to the application may be analyzed to determine a defect associated with the application. Further, a code of the application may be modified to correct the defect associated with the application.

Abstract: A method for component discovery from source code may include receiving source code, and determining business classes by excluding packages and classes in the source code identified as belonging to a presentation layer, as belonging to a data access layer, as models and/or as utilities. The method may further include extracting multi-dimensional features from the business classes, estimating similarity for business class pairs based on the extracted multi-dimensional features, clustering the business classes based on the similarity and mapping functional concepts to the clusters. The clusters generated by the clustering may represent components of the source code. The method may also include determining interfaces for the components based on the clustering.

Abstract: Systems and methods for distributed control and energy management of one or more communities of energy-consuming units may include aggregation of consumption data from units, and determining per-unit electricity consumption based thereon, including consumption of backup power provided by a community during periods of time of poor quality (brownouts) or blackouts of a utility. A system may calculate and assess to respective units per-unit costs for such backup power. A system may also issue a command or alert to units to carry out one or both of community electricity usage objectives and electricity quotas required by the utility, which may be determined through execution of rules.

Abstract: Multi-data analysis based proactive defect detection and resolution may include analyzing operational data for an application to determine whether a functionality related to the application is below a predetermined threshold associated with the functionality related to the application, and based on the analysis, generating an indication to perform defect analysis related to the functionality related to the application. A sentiment analysis may be performed on consumer data related to the application to determine a sentiment of the consumer data related to the application, and a natural language processing (NLP) analysis may be performed on the consumer data related to the application to determine a function associated with a negative sentiment. Application code and process data related to the application may be analyzed to determine a defect associated with the application. Further, a code of the application may be modified to correct the defect associated with the application.

Abstract: According to an example, source code flow analysis may include receiving source code for an application, and identifying virtual flow documents for the application from the source code. The virtual flow documents may represent ordered sequences of method calls for the application. The source code flow analysis may further include extracting features of the virtual flow documents, determining similarity between the virtual flow documents by estimating similarities for the extracted features to determine a flow-to-flow similarity, and clustering the virtual flow documents based on the flow-to-flow similarity. The flow-to-flow similarity may be further used, for example, to generate highest priority virtual flow documents and methods for the source code.

Abstract: Multi-data analysis based proactive defect detection and resolution may include analyzing operational data for an application to determine whether a functionality related to the application is below a predetermined threshold associated with the functionality related to the application, and based on the analysis, generating an indication to perform defect analysis related to the functionality related to the application. A sentiment analysis may be performed on consumer data related to the application to determine a sentiment of the consumer data related to the application, and a natural language processing (NLP) analysis may be performed on the consumer data related to the application to determine a function associated with a negative sentiment. Application code and process data related to the application may be analyzed to determine a defect associated with the application. Further, a code of the application may be modified to correct the defect associated with the application.

Abstract: According to an example, source code flow analysis may include receiving source code for an application, and identifying virtual flow documents for the application from the source code. The virtual flow documents may represent ordered sequences of method calls for the application. The source code flow analysis may further include extracting features of the virtual flow documents, determining similarity between the virtual flow documents by estimating similarities for the extracted features to determine a flow-to-flow similarity, and clustering the virtual flow documents based on the flow-to-flow similarity. The flow-to-flow similarity may be further used, for example, to generate highest priority virtual flow documents and methods for the source code.

Abstract: A process modeling rule validation system includes a graphical user interface module and a rule creation module. The graphical user interface module includes a display and at least one user input mechanism. The graphical user interface module displays information representative of a plurality of rule creation elements via the display and receives user input via the user input mechanism in response thereto. At least one of the rule creation elements can be configured based on the user input and in accordance with best practices in process modeling. The rule creation module, which is operatively connected to the graphical user interface module, generates a process modeling rule in response to at least one of the rule creation elements being configured.

Abstract: A method for component discovery from source code may include receiving source code, and determining business classes by excluding packages and classes in the source code identified as belonging to a presentation layer, as belonging to a data access layer, as models and/or as utilities. The method may further include extracting multi-dimensional features from the business classes, estimating similarity for business class pairs based on the extracted multi-dimensional features, clustering the business classes based on the similarity and mapping functional concepts to the clusters. The clusters generated by the clustering may represent components of the source code. The method may also include determining interfaces for the components based on the clustering.

Abstract: A method for component discovery from source code may include receiving source code, and determining business classes by excluding packages and classes in the source code identified as belonging to a presentation layer, as belonging to a data access layer, as models and/or as utilities. The method may further include extracting multi-dimensional features from the business classes, estimating similarity for business class pairs based on the extracted multi-dimensional features, clustering the business classes based on the similarity and mapping functional concepts to the clusters. The clusters generated by the clustering may represent components of the source code. The method may also include determining interfaces for the components based on the clustering.

Abstract: A user interface component is displayed according to an implementation language independent description. An implementation language dependent specification for the user component is created from the implementation language independent description. The user interface component is created and displayed according to the implementation language dependent specification.

Abstract: Workflow test case generation may include receiving an intermediate output of a workflow. The intermediate output of the workflow may reference a plurality of workflow building blocks that provide a specified functionality at each step of the workflow. Workflow definitions of the intermediate output of the workflow may be analyzed to generate a graph structure that represents the workflow. Building block attributes that include an identification of attributes for the plurality of workflow building blocks, and the graph structure may be analyzed to generate building block and attribute information for the workflow. The building block and attribute information and the graph structure may be analyzed to generate test paths for the workflow. The building block and attribute information, the test paths, and a plurality of test case templates may be analyzed to generate test artifacts that include testing to be performed for the workflow.

Abstract: A method for component discovery from source code may include receiving source code, and determining business classes by excluding packages and classes in the source code identified as belonging to a presentation layer, as belonging to a data access layer, as models and/or as utilities. The method may further include extracting multi-dimensional features from the business classes, estimating similarity for business class pairs based on the extracted multi-dimensional features, clustering the business classes based on the similarity and mapping functional concepts to the clusters. The clusters generated by the clustering may represent components of the source code. The method may also include determining interfaces for the components based on the clustering.

Abstract: According to an example, source code flow analysis may include receiving source code for an application, and identifying virtual flow documents for the application from the source code. The virtual flow documents may represent ordered sequences of method calls for the application. The source code flow analysis may further include extracting features of the virtual flow documents, determining similarity between the virtual flow documents by estimating similarities for the extracted features to determine a flow-to-flow similarity, and clustering the virtual flow documents based on the flow-to-flow similarity. The flow-to-flow similarity may be further used, for example, to generate highest priority virtual flow documents and methods for the source code.

Abstract: A method for component discovery from source code may include receiving source code, and determining business classes by excluding packages and classes in the source code identified as belonging to a presentation layer, as belonging to a data access layer, as models and/or as utilities. The method may further include extracting multi-dimensional features from the business classes, estimating similarity for business class pairs based on the extracted multi-dimensional features, clustering the business classes based on the similarity and mapping functional concepts to the clusters. The clusters generated by the clustering may represent components of the source code. The method may also include determining interfaces for the components based on the clustering.