Examples described herein include systems and methods for providing user flow insights on a graphical user interface (GUI) for application process implementations across a network. The GUI can visualize successful and unsuccessful implementations of processes of an enterprise application. This can help administrative users more quickly identify issues with the application, which can report user flow information to a server. The GUI can present a first visual overlay comparing successful and unsuccessful user flows over specified time periods. Groups of successful and unsuccessful user flows can be displayed on top of one another for immediate relative visualization. Additionally, user flows can be grouped according to application processes and summarized in a second visual overlay. The second visual overlay can represent all user flows for an application process and be accompanied by a table of user flow entries, which may be expanded to reveal discrete events defining individual user flows.

A method of performing error analysis in a system comprising microservices comprises identifying a root cause error span from among a plurality of error spans for a trace associated with a user-request, wherein an error span is a span that returns an error to a microservice initiating a call resulting in the span, and wherein a root cause error span is an error span associated with an error originating microservice. The method further comprises determining a call path associated with the root cause error span, where the call path comprises a chain of spans starting at the root cause error span, and where each subsequent span in the chain is a parent span of a prior span. Subsequently the method comprises mapping each span in the chain to a span error frame to create an error stack and rendering an image of the error stack.

An example method includes receiving, by an application server system and from one or more client computing devices, application error data associated with at least one error that occurred during execution of at least one application, receiving mapping data that provides a mapping between (i) library-dependent source code of the application(s) and (ii) at least one third-party library from which the library-dependent source code is loaded during execution of the application(s), determining, based on the application error data and the mapping data, a match between the library-dependent source code and at least one portion of the application error data, attributing the at least one error to the at least one third-party library, generating library error data associated with the at least one third-party library, and sending, to at least one third-party library development system, the library error data.

One or more processing devices access a service definition for a service provided by one or more entities that each produce machine data or about which machine data is generated. The service definition identifies the entities that provide the service and, for each entity, definitional information includes information for identifying machine data pertaining to that entity. The processing devices access a key performance indicator (KPI) for the service that is defined by a search query that produces a value derived from the machine data pertaining to the entities identified in the service definition. The value indicates how the service is performing at a point in time or during a period of time and indicates a state of the KPI. A graphical interface is displayed and an indication of at least one threshold, which defines an end of a range of values representing a state of the KPI, for the KPI is received.

The present disclosure provides a method, a system, a device for processing operation and maintenance data. The method includes acquiring a plurality of types of the operation and maintenance data of a target server, converting the operation and maintenance data into quantized data of each dimension according to a preset rule, and determining quality evaluation information of the target server according to the quantized data of each dimension. The technical solution provided by the present application may improve evaluation precision of product performance.

A device may perform a testing operation on a device under test. The device may obtain test result data based on performing the testing operation on the device under test. The device may identify a user device that is to receive the test result data associated with the device under test from the device and via a network. The device may be in communication with a set of user devices via the network. The set of user devices may include the user device. The user device may control operation of the device. The device may determine network condition information associated with the user device and the network. The device may provide, using a technique that is based on the network condition information, the test result data to the user device. The user device may receive the test result data based on controlling operation of the device.

Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

A device may generate a hypergraph for a plurality of cells included in a communications network. The device may identify one or more parameters for allocating operating transmission frequencies to the plurality of cells. The plurality of cells may correspond to vertices of the hypergraph, and one or more cumulative transmission interference regions, associated with the plurality of cells, may correspond to hyperedges of the hypergraph. The device may generate a constraint model based on the hypergraph and the one or more parameters. The device may determine, using a quantum solver, one or more minimum energy states of the constraint model. The one or more minimum energy states may correspond to respective operating transmission frequency allocation configurations for the plurality of cells. The device may assign, based on a minimum energy state of the one or more minimum energy states, operating transmission frequencies to the plurality of cells.

Techniques for performing load testing and profiling of services in a provider network are described. A load testing and profiling service is disclosed that analyzes profile data generated by a service and generates profile results associated with the service when the service operates at varying and/or increasing load capacities. The profile results are indicative of the performance of one or more functions performed by a service when the service operates at different load capacities. In certain embodiments, the load testing and profiling service can be invoked as part of a Continuous Deployment/Continuous Integration (CD/CI) environment that executes a load test against a test stack (e.g., test requests) before, for example, promoting code to production. For instance, the load testing and profiling service may be invoked as a step in a code deployment pipeline, e.g., for deploying a software product to a test environment, or to a production environment.

The automatic identification of execution behavior(s) of software. This automatic identification is based on a historical analysis of execution records to identify a particular pattern that represents an execution behavior. In order to automatically identify an execution behavior present within particular software, an execution record (or perhaps multiple execution records) representing the execution of that particular software may be accessed. Based on finding the particular pattern within the execution record (or one, some, or all of the multiple execution records) representing the execution of that particular software, the computing system may automatically identify that the execution behavior is present within the software. This may dramatically assist in modifying that execution behavior.