Disclosed herein are embodiments of systems, methods, and products for modernizing and optimizing legacy software. A computing device may perform an automated runtime performance profiling process. The performance profiler may automatically profile the legacy software at runtime, monitor the memory usage and module activities of the legacy software, and pinpoint/identify a subset of inefficient functions in the legacy software that scale poorly or otherwise inefficient. The computing device may further perform a source code analysis and refactoring process. The computing device may parse the source code of the subset of inefficient functions and identify code violations within the source code. The computing device may provide one or more refactoring options to optimize the source code. Each refactoring option may comprise a change to the source code configured to correct the code violations. The computing device may refactor the source code based on a selected refactoring option.

Disclosed herein are embodiments for managing a task including one or more skills. A server stores a virtual environment, software agents configured to collect data generated when a user interacts with the virtual environment to perform the task, and a predictive machine learning model. The server generates virtual entities during the performance of the task, and executes the predictive machine learning model to configure the virtual entities based upon data generated when the user interacts with the virtual environment. The server generates the virtual environment and the virtual entities configured for interaction with the user during display by the client device, and receives the data collected by the software agents. The system displays a user interface at the client device to indicate a measurement of each of the skills during performance of the task. The server trains the predictive machine learning model using this measurement of skills during task performance.

An agent device 1 of a monitoring system 5 includes: an acquisition unit 22 that sequentially acquires monitored values from a processing unit 21; a removal unit 23 that generates denoised monitored values by removing noise from the monitored values; and a transmission unit that transmits the denoised monitored values to a manager device 2. The manager device 2 includes a determination unit 62 that determines the monitoring interval, at which monitored values are acquired from the agent device 1, by referencing the denoised monitored values acquired from the agent device 1.

The present disclosure pertains to systems and methods to monitor communication ports. In one embodiment, a system may include a host device that generates a first flow of traffic to send via a host device communication port. A security dongle may receive the first flow of traffic via a security dongle communication port in communication with the host device communication port. The security dongle may generate a second flow of traffic. The security dongle may transmit the second flow of traffic through the security dongle communication port. The host device may receive the second flow of traffic from the host device communication port and may generate an alarm when the second flow of traffic deviates from an expected response. The communication between the host device and the security dongle allows the host device to detect when the security dongle is disconnected from the host device.

Systems and methods for proactive support of computing assets are presented. In contrast to existing techniques of reactive support, the proactive support techniques disclosed herein automatically collect operating data from a plurality of computing devices, analyze the operating data to identify predictive indicators associated with error conditions, identify a subset of affected computing devices that match the predictive indicators, and execute corrective scripts to remediate or avoid such error conditions before problems are experienced on the affected computing devices. The operating data may be used to train a machine learning model in order to identify the predictive indicators associated with each error condition. In some embodiments, the corrective scripts may be automatically generated to adjust operating parameters or applications of the affected computing devices based upon the identified predictive indicators.

When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

The present disclosure discloses a method and apparatus of monitoring an interface performance of a distributed application, a device and a storage medium, which relates to a field of computer technology, in particular to a field of cloud platform. The method includes: in case of detecting a caller request for calling an interface of the distributed application, obtaining a performance data of the interface for responding the caller request; updating a performance data distribution characteristic of the interface according to the performance data of the interface for responding the caller request, so as to obtain an updated performance data distribution characteristic; and monitoring the interface performance of the distributed application, according to the updated performance data distribution characteristic of the interface.

Systems and methods for analyzing and identifying applications using a crawler. The method includes receiving first data from an application registry and second data from an application image store. The method also includes identifying a first set of candidate applications based on the first data and identifying a second set of candidate applications based on the second data. The method further includes storing the first set of candidate applications and the second set of candidate applications in a database. The method also includes generating for display on a user interface a first list of the first set of candidate application and a second list of the second set of candidate applications. The method further includes receiving third data from a user via the user interface and identifying a third set of candidate applications based on the first data and the third data.

A quality score for a computer application release is determined using a first number of unique users who have launched the computer application release on user devices and a second number of unique users who have encountered at least once an abnormal termination with the computer application release on user devices. Additionally or optionally, an application quality score can be computed for a computer application based on quality scores of computer application releases that represent different versions of the computer application. Additionally or optionally, a weighted application quality score can be computed for a computer application by further taking into consideration the average application quality score and popularity of a plurality of computer applications.

Aspects extend to methods, systems, and computer program products for intent-based interactions with cluster resources. One or more computer systems are joined in a computer system cluster to provide defined computing functionality (e.g., storage, compute, network, etc.) to an external system. In one aspect, a data collection intent facilitates collection and aggregation of data to form a health report for one or more components of the computer system cluster. In another aspect, a command intent facilitates implementing a command at one or more components of the computer system cluster. Services span machines of the computer system cluster to abstract lower level aspects of data collection and aggregation and command implementation for higher level aspects of data collection and aggregation and command implementation. Services can be integrated into an operating system to relieve users from having to have operating system knowledge.