Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.

In one implementation, a method for recording an XR environment. The method includes: presenting, via the display device, a graphical environment with one or more virtual agents, wherein the graphical environment corresponds to a composition of extended reality (XR) content, including the one or more virtual agents, and an image stream of a physical environment captured from a first point-of-view (POV) of the physical environment; detecting, via the one or more input devices, a user input selecting a first virtual agent from among the one or more virtual agents; and in response to detecting the user input, recording a plurality of data streams associated with the graphical environment including a first image stream of the graphical environment from the first POV and one or more data streams of the graphical environment from a current POV of the first virtual agent.

Operations may include obtaining a plurality of posts from one or more web sites, each post including a respective buggy snippet of source code that includes a corresponding error. The operations may also include generating a plurality of bug patterns from the plurality of posts in which each respective bug pattern corresponds to a respective buggy snippet and indicates a corresponding bug scenario that leads to the corresponding error of the respective buggy snippet that corresponds to the respective bug pattern. The operations may also include determining similarities with respect to the respective bug patterns and selecting, based on the similarity determinations, a first bug pattern of the plurality of bug patterns for revision. In addition, the operations may include obtaining a revised bug pattern that is a revised version of the first bug pattern.

Systems and methods for providing an unsupervised classification model by converting unsupervised data to supervised data. In one implementation, a processing device can receive an unlabeled dataset comprising one or more data records. The processing device can divide the unlabeled dataset into a plurality of groups. The processing device can then generate, for each group of the plurality of groups, a corresponding label. The processing device can generate a labeled dataset by assigning, to each group of the plurality of groups, the corresponding label. The processing device can then classify the labeled dataset using a classification model.

Systems and methods for providing an unsupervised classification model by converting unsupervised data to supervised data. In one implementation, a processing device can receive an unlabeled dataset comprising one or more data records. The processing device can divide the unlabeled dataset into a plurality of groups. The processing device can then generate, for each group of the plurality of groups, a corresponding label. The processing device can generate a labeled dataset by assigning, to each group of the plurality of groups, the corresponding label. The processing device can then classify the labeled dataset using a classification model.

Disclosed herein are techniques for identifying software interdependencies based on functional line-of-code behavior and relation models. Techniques include identifying a first portion of executable code associated with a first controller; accessing a functional line-of-code behavior and relation model representing functionality of the first portion of executable code and a second portion of executable code; determining, based on the functional line-of-code behavior and relation model, that the second portion of executable code is interdependent with the first portion of executable code; and generating, based on the determined interdependency, a report identifying the interdependent first portion of executable code and second portion of executable code.

An apparatus, system, method, and computer-readable recording medium perform intelligent upgrade to a debug load operation in an electronic device. A key is set in advance via a Management Information Base (MIB), which defines execution of a debug load operation for the electronic device. When a debug load operation is requested, the electronic device validates the key via the MIB, and sets a time period for execution of the debug load operation. The electronic device upgrades from an official release load operation to the debug load operation and executes the debug load operation. After the expiration of the time period, the electronic device automatically upgrades back to the official release from the debug load operation in the electronic device after expiration. The debug load operation collects information related to the electronic device and transmits the information to an external server for storage.

A representation of a category of task offloaders is stored, in response to receiving a descriptor of the category, in a database of categories of offloaders which can be attached to servers of one or more classes. An indication of server configurations which include a task offloader of the category is provided via programmatic interfaces. A task is executed at a task offloader of a server with one of the server configurations.

In one embodiment, a host processor includes a configuration circuit that, in response to identification of a first device capable of debugging a second device, is to configure a switch to enable device-to-device messaging between the first device and the second device, the device-to-device messaging comprising at least one of debug messaging or test messaging to be communicated without host processor involvement. Other embodiments are described and claimed.

Systems and methods for implementing a build-time, automatic, exporter configuration rule generator that removes the need for manual definition of exporter configurations are described. A processing device may perform a scan of source code of an application to identify one or more classes of the application, each of the one or more classes enabling an exporter to access metrics generated by the class. For each of the one or more classes, the processing device may analyze source code of the class with a set of templates and heuristics to generate a set of configuration rules for the class. The processing device may then generate an exporter configuration for the exporter based on the set of configuration rules for each of the one or more classes.