Abstract: Described herein are techniques for replaying a session of an application executed by a device (e.g., a mobile device). The session may be a session in which a user was interacting with the application. The techniques replay a session by replicating a sequence of visualizations that were rendered in an application GUI by the device in a replay GUI displayed on another device. The techniques replicate visualizations by: (1) accessing data associated with the visualizations from the device executing the application; and (2) replicating the visualizations in the replay GUI to provide a replay. A user of the other device viewing the replay GUI will see a replay of the visualizations rendered in the application GUI in the session.

Abstract: Described herein are techniques for capturing data from devices to generate replays of sessions of a software application executed by the devices. The techniques use data obtained from the device to determine whether certain events in which a session replay is to be generated have occurred in a session of the software application. When occurrence of such an event is detected during the session of the software application, the techniques determine to trigger collection of session replay data. In this case, the session replay data may be stored and/or transmitted from the device for storage, and subsequently used to generate a session replay. If none of the events are detected, then session replay data may not be transmitted. Accordingly, the techniques can selectively allocate resources (e.g., compute, storage, and/or communication resources) for sessions in which events are detected.

Abstract: Described herein are techniques that use machine learning to triage issues by classifying the issues into impact levels. Described herein are also techniques for generating a natural language description of issues that occur in sessions of a software application. The techniques collect data during sessions in which a user is interacting with the software application. The techniques process the data collected during the sessions using a language model to obtain natural language descriptions of issues that occur in the sessions.

Abstract: Described herein are techniques that use machine learning to triage issues by classifying the issues into impact levels. Described herein are also techniques for generating a natural language description of issues that occur in sessions of a software application. The techniques collect data during sessions in which a user is interacting with the software application. The techniques process the data collected during the sessions using a language model to obtain natural language descriptions of issues that occur in the sessions.

Abstract: Described herein are techniques for replaying a session of an application executed by a device (e.g., a mobile device). The session may be a session in which a user was interacting with the application. The techniques replay a session by replicating a sequence of visualizations that were rendered in an application GUI by the device in a replay GUI displayed on another device. The techniques replicate visualizations by: (1) accessing data associated with the visualizations from the device executing the application; and (2) replicating the visualizations in the replay GUI to provide a replay. A user of the other device viewing the replay GUI will see a replay of the visualizations rendered in the application GUI in the session.

Abstract: Described herein are techniques that use machine learning to triage issues by classifying the issues into impact levels. Described herein are also techniques for generating a natural language description of issues that occur in sessions of a software application. The techniques collect data during sessions in which a user is interacting with the software application. The techniques process the data collected during the sessions using a language model to obtain natural language descriptions of issues that occur in the sessions.

Abstract: Described herein are techniques for capturing data from devices to generate replays of sessions of a software application executed by the devices. The techniques use data obtained from the device to determine whether certain events in which a session replay is to be generated have occurred in a session of the software application. When occurrence of such an event is detected during the session of the software application, the techniques determine to trigger collection of session replay data. In this case, the session replay data may be stored and/or transmitted from the device for storage, and subsequently used to generate a session replay. If none of the events are detected, then session replay data may not be transmitted. Accordingly, the techniques can selectively allocate resources (e.g., compute, storage, and/or communication resources) for sessions in which events are detected.

Abstract: Described herein are techniques for replaying a session of an application executed by a device (e.g., a mobile device). The session may be a session in which a user was interacting with the application. The techniques replay a session by replicating a sequence of visualizations that were rendered in an application GUI by the device in a replay GUI displayed on another device. The techniques replicate visualizations by: (1) accessing data associated with the visualizations from the device executing the application; and (2) replicating the visualizations in the replay GUI to provide a replay. A user of the other device viewing the replay GUI will see a replay of the visualizations rendered in the application GUI in the session.

Abstract: Described herein are techniques for capturing data from devices to generate replays of sessions of a software application executed by the devices. The techniques use data obtained from the device to determine whether certain events in which a session replay is to be generated have occurred in a session of the software application. When occurrence of such an event is detected during the session of the software application, the techniques determine to trigger collection of session replay data. In this case, the session replay data may be stored and/or transmitted from the device for storage, and subsequently used to generate a session replay. If none of the events are detected, then session replay data may not be transmitted. Accordingly, the techniques can selectively allocate resources (e.g., compute, storage, and/or communication resources) for sessions in which events are detected.

Abstract: Described herein are techniques for replaying a session of an application executed by a device (e.g., a mobile device). The session may be a session in which a user was interacting with the application. The techniques replay a session by replicating a sequence of visualizations that were rendered in an application GUI by the device in a replay GUI displayed on another device. The techniques replicate visualizations by: (1) accessing data associated with the visualizations from the device executing the application; and (2) replicating the visualizations in the replay GUI to provide a replay. A user of the other device viewing the replay GUI will see a replay of the visualizations rendered in the application GUI in the session.

Abstract: Described herein are techniques that use machine learning to triage issues by classifying the issues into impact levels. Described herein are also techniques for generating a natural language description of issues that occur in sessions of a software application. The techniques collect data during sessions in which a user is interacting with the software application. The techniques process the data collected during the sessions using a language model to obtain natural language descriptions of issues that occur in the sessions.

Abstract: Described herein are techniques that use machine learning to triage issues by classifying the issues into impact levels. Described herein are also techniques for generating a natural language description of issues that occur in sessions of a software application. The techniques collect data during sessions in which a user is interacting with the software application. The techniques process the data collected during the sessions using a language model to obtain natural language descriptions of issues that occur in the sessions.

Abstract: Described herein are techniques for capturing data from devices to generate replays of sessions of a software application executed by the devices. The techniques use data obtained from the device to determine whether certain events in which a session replay is to be generated have occurred in a session of the software application. When occurrence of such an event is detected during the session of the software application, the techniques determine to trigger collection of session replay data. In this case, the session replay data may be stored and/or transmitted from the device for storage, and subsequently used to generate a session replay. If none of the events are detected, then session replay data may not be transmitted. Accordingly, the techniques can selectively allocate resources (e.g., compute, storage, and/or communication resources) for sessions in which events are detected.