International Business Machines Corporation

Li Li Guan

Zhe Yan

Rong Zhao

Li Bo Zhang

Hao Jia Li

Ke Zhang

Yuan Jin

Jing Jing Zhang

Jing Zhang

Peng Tang

Hao Xiang Wu

Embodiments relate to graph-based design mapping and function route generation. A computer-implemented method includes analyzing first user interfaces and second user interfaces to identify features of each user interface, where the second user interfaces include at least one change to the first user interfaces. A first graph representing the features of the first user interfaces and a second graph representing the features of the second user interfaces are generated. A function mapping is determined based on a similarity calculation between the first graph and the second graph. A node that satisfies an influence threshold is recommended to do limited manual labeling. The function mapping is updated based on the labeling. A visualization of the function mapping is created between the first user interfaces and the second user interfaces. Function route generation can be performed.

Graph-based design mapping and function route generation

An embodiment for detecting anomalous data using dependency modeling. The embodiment may, within a target data environment, identify references between data contained in one or more data files. The embodiment may determine dependency relationships between data fields in the data contained in the one or more data files. The embodiment may construct computational graphs depicting the determined dependency relationships as series of related data fields. The embodiment may identify a series of associated computational graphs within the constructed computational graphs. The embodiment may calculate abnormality degree values for each of the data fields within the constructed computation graphs. The embodiment may, in response to detecting an anomalous data field having a calculated abnormality degree value above a threshold value, calculating contribution values for a series of associated component data fields to identify a root cause for the detected anomalous data field.

Detecting temporal anomalous data using dependency modeling

An approach for assisting software developers to fill the data gap and reproducing client application issue using reported reproducing steps is disclosed. The approach involves constructing service request/response sequence (from the developer's side based on the limited data) from customer's reproducing steps and developer's running inspection result and perform UI-service mapping, redirecting service call to data simulator which replay the service request and response, and adjusting service response with multiple data sources and policies. Lastly, the approach can transform the issue to an automatic test script with the service response in a mockup function (e.g., can be used to extend test data and enhance test cases).

Reproduction client application issue based on layered data replay

A computer-implemented method includes: extracting, by one or more processors, media data from an information technology ticket; extracting, by one or more processors, a plurality of data elements from the media data; and generating, by one or more processors, a ticket knowledge graph based on the plurality of data elements, wherein a node of the ticket knowledge graph represents a data element, and an edge between a first node and a second node in the ticket knowledge graph represents a correlation between a first data element represented by the first node and a second data element represented by the second node.

Ticket knowledge graph enhancement

Aspects of the present disclosure relate to support content generation. An issue description is received from a user. A software feature associated with the issue description is identified using a trained classification model. A base image associated with the software feature is obtained. The base image is modified to add information indicated in the issue description, wherein the modified base image is generated support content. The generated support content is transmitted.

User support content generation

A method, a computer program product, and a computer system guide a user through dimensional relationships. The method includes receiving a plurality of images of a unit of a perspective of a user. When a number of corresponding three-dimensional points between a current image and a previous image is less than a registration threshold to perform a three-dimensional registration operation, the method includes performing a three-dimensional data augmentation operation based on two-dimensional data from the current image and the previous image to generate extended corresponding three-dimensional points. The method includes determining a three-dimensional transform function between the current image and the previous image based on the extended corresponding three-dimensional points. The method includes generating annotations to be shown for the unit in the current image based on the three-dimensional transform function, the annotations being shown as a virtual rendering in a mixed reality environment viewed by the user.

Self guidance based on dimensional relationship

A method, computer system, and a computer program product for AR guidance is provided. The present invention may include detecting a plurality of objects in a video recording associated with completing a task. The present invention may include generating a plurality of three-dimensional (3D) object models based on scanning a plurality of real objects in a task space. The present invention may include matching the detected plurality of objects in the video recording with the generated plurality of 3D object models representing the plurality of real objects in the task space. The present invention may include generating, based on the video recording, an augmented reality (AR) guidance model for completing the task, wherein the generated AR guidance model replaces the detected plurality of objects in the video recording with the generated plurality of 3D object models representing the plurality of real objects in the task space.

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Beijing, China

Li Li Guan