Patent classifications
G06F8/355
BIDIRECTIONAL EVALUATION FOR GENERAL- PURPOSE PROGRAMMING
A method of facilitating bidirectional programming of a user may include receiving an original program source code and evaluating the original program source code in the forward direction to generate a program output. The evaluation may occur in a programming environment. The program output may be displayed, and an indication of the user corresponding to modifying the program output may be received. The modified program output may be evaluated to generate an updated program source code, wherein the updated program source code, when evaluated, may generate the modified program output. The modified program output may be displayed in a display device of the user. A computing system including a bidirectional programming environment may also be included.
CONTENT DEVELOPMENT AND MANAGEMENT
A development engine may enable a developer to customize a user experience using an intuitive developer interface. A rules engine may provide constructs to a card engine in the form of card definitions, which the card engine may evaluate using facts obtained from a facts controller. The evaluated card definitions are cards that may be output for presentation via user equipment. Variants may be assigned weights which can be set or changed dynamically by the card engine substantively and in real time based on factors such as user behavior, account condition, promotions or offerings. The card engine may make content decisions proximate to events occurring to the user. The presentation of the cards may be changed substantively and in real time in accordance with the setting or changes in variants.
BIDIRECTIONAL EVALUATION FOR GENERAL- PURPOSE PROGRAMMING
A method of facilitating bidirectional programming of a user may include receiving an original program source code and evaluating the original program source code in the forward direction to generate a program output. The evaluation may occur in a programming environment. The program output may be displayed, and an indication of the user corresponding to modifying the program output may be received. The modified program output may be evaluated to generate an updated program source code, wherein the updated program source code, when evaluated, may generate the modified program output. The modified program output may be displayed in a display device of the user. A computing system including a bidirectional programming environment may also be included.
Authoring automated test suites using artificial intelligence
Methods and apparatus are described by which artificial intelligence (AI) is used to enable the rapid development of reliable test suites for web and mobile applications. An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states. A subset of these flows is then identified as being representative of the functionality of the AUT. The interactions between the AI agent and the AUT that define these identified flows form the basis for the test suite.
Method and system for vehicle platform validation
A method of evaluating compatibility of a first system component of a vehicle, the method including providing a database including vehicle platform configuration information including configuration information about two or more vehicle models, wherein each vehicle model includes one or more aspect domains and each aspect domain includes one or more system components. Each system component includes configuration information. At least two system components of two different vehicle models belong to the same aspect domain. The method also includes determining a compatibility result between said first system component and at least one other system component in said vehicle platform by comparing the configuration information of said first system component to the configuration information of said at least one other system component.
AUTHORING AUTOMATED TEST SUITES USING ARTIFICIAL INTELLIGENCE
Methods and apparatus are described by which artificial intelligence (AI) is used to enable the rapid development of reliable test suites for web and mobile applications. An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states. A subset of these flows is then identified as being representative of the functionality of the AUT. The interactions between the AI agent and the AUT that define these identified flows form the basis for the test suite.
System and computer-implemented method for bidirectional translation between diagramming and implementation tools
A system and computer-implemented method for translating diagramming data from a diagramming tool into implementation data for direct implementation by an implementation tool for implementing a component, and for translating the implementation data back into the diagramming data for direct visualization by the diagramming tool. The diagramming tool generates the diagramming data. A translating tool receives the diagramming data, reads, validates, and translates it directly into the implementation data, and saves the implementation data in an export file. The implementation tool receives the export file and uses the implementation data to implement the component. The translating tool can also translate the implementation data directly back into the diagramming data, and save the diagramming data in an import file. The diagramming tool receives the import file and uses the diagramming data to visualize the diagram of the component. The component may be physical or virtual, and part of information technology infrastructure.
Providing cognitive intelligence across continuous delivery pipeline data
A method, system and computer program product for detecting potential failures in a continuous delivery pipeline. A machine learning model is created to predict whether changed portion of codes under development at various stages of the continuous delivery pipeline will result in a pipeline failure. After creating the machine learning model, log file(s) may be received that were generated by development tool(s) concerning a changed portion of code under development at a particular stage of the continuous delivery pipeline. The machine learning model provides relationship information between the log file(s) and the changed portion of code. A message is then generated and displayed based on this relationship information, where the message may provide a prediction or a recommendation concerning potential failures in the continuous delivery pipeline. In this manner, the potential failures in the continuous delivery pipeline may be prevented without requiring context switching.
Application builder with automated data objects creation
Techniques for simplifying the process of building an application and making changes to the application. The process of creating and editing an application is simplified such that a non-technical user can build and edit applications without having any programming or technical knowledge. An infrastructure is provided for building an application that enables a user to create an application by simply designing a user interface for the application using one or more provided user interface (UI) components. A user can build a full executable application by simply using UI components and the back end data objects and schemas used for the application are automatically created and updated by the infrastructure. Due to the automatic creation of the data objects and schemas and the automatic binding of these to the UI components of the application, the application being built is able to run or execute while being built and/or edited.
Hardware device based software selection
A method and system for improving an operation of an automated IT system is provided. The method includes identifying software applications associated with requirements of processes executed by a hardware device with respect to an IT system. An ordered set of software solutions for modifying the software applications is generated. Sampling software code is generated by applying a sampling technique for enabling the hardware device to execute learning software code with respect to database systems. The sampling software code is executed for enabling an automated learning process applying a feature learning technique for identifying a set of software applications and enabling an evaluation of the software applications with respect to organizational parameters for identifying an organizational fitness for the set of software applications. A software application and associated feature are identified and the software application is executed resulting in improved operation of the hardware device.