Provided is a method for testing a microservice application with at least one microservice with at least one application programming interface, including: reading characteristic data of the application programming interface of the microservice of the microservice application and ascertaining at least one endpoint of the application programming interface; automatically generating an execution script on the basis of the characteristic data of the application programming interface; automatically generating a test infrastructure, wherein the test infrastructure includes at least one client entity; executing the execution script and transmitting the data query of the execution script by the client entity to the application programming interface of the microservice and receiving corresponding response data of the microservice by the client entity; and ascertaining the transfer characteristic by the client entity.

In some examples, a system comprises a network interface; a storage device comprising machine-readable instructions; and a processor coupled to the network interface, the processor to access the storage device, wherein execution of the machine-readable instructions causes the processor to: collect crash event data; categorize the crash event data by an application executing when the crash event occurred; identify a crash event corresponding to the crash event data; create an identifier for the crash event; compare the identifier of the crash event to a list of work items, wherein each work item has an identifier; and update the list of work items based on the comparison.

Abstract interpretation based static analysis tools use relational/non-relational abstract domains to verify program properties. Precision and scalability of analysis vary basis usage of abstract domains. K-limited path-sensitive interval domain is an abstract domain that was conventionally proposed for analysis on industry strength programs. The domain maintains variables' intervals along a configurable K subsets of paths at each program point, which implicitly provides co-relation among variables. When the number of paths at the join point exceeds K, set of paths are partitioned into K subsets, arbitrarily, which results in loss of precision required to verify program properties. To address the above problem, embodiments of the present disclosure provide selective merging of paths in such a way that the intervals computed help verifying more properties. The selective path-sensitive method of the present disclosure is based on the knowledge of variables whose values influence the verification outcome of program properties.

Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments for monitoring long-standing computing resources. An apparatus may operate by receiving a cloud monitoring notification, where the cloud monitoring notification may indicate an occurrence of a monitored condition. The apparatus may then operate by scanning a cluster computing system for resource having a client assigned resource identifier and a computing resource attribute based on a resource identifier scan parameter and a resource attribute scan parameter. The apparatus may further operate by generating a resource notification request based on the scanning of the cluster computing system and transmitting the resource notification request to a communications system to notify a user that the resource has a computing resource attribute that match the resource attribute scan parameter.

Techniques for in-band modification of event notification preferences for server events are provided. One method comprises obtaining an event notification; providing the event notification to a target device based on rule-based preferences of a user associated with the target device;

obtaining a reply to the event notification from the target device, wherein the reply comprises event preferences of the user; and updating the rule-based preferences of the user based on the event preferences of the user. The updating of the rule-based preferences of the user may comprise creating, modifying and/or canceling at least one event preference rule of the user. A plurality of the event preference rules matching the event notification may be resolved in an order determined by one or more event preference rule resolution criteria.

Techniques are described herein for probabilistic monitoring of high-frequency, low-latency database queries. In some embodiments, a probabilistic query monitoring system periodically samples active database sessions. For example, the system may generate sample data every one second or at some other sampling rate for each database session that is currently active. The sample data may include a mapping between query identifiers to sample counter values that are extracted at different sample intervals. The system may then estimate performance metrics for the set of active database based on the counter values sampled across consecutive sample intervals.

In an example embodiment, a solution is provided that detects performance degradation of a particular functionality or an overall system problem using machine learning, Mann-Kendall tests, and correlation tests. After a problem has been automatically detected, the first steps of a root cause analysis may be automatically performed, indicating whether, for example non-optimal ABAP coding, a database issue, or hardware or software bottleneck, or some combination thereof. This approach allows a system to rapidly identify a system performance problem and its root cause by combing several data sources. One can see immediately, for example, whether an ABAP code change, an expensive SQL statement, or the combination of both led to an increase in the average response times of a transaction over time.

Techniques are provided for analyzing events incoming through a message broker and configuring a database schema for storing the events based on the analysis. The analysis is performed on all the attributes of the incoming events with reference to a primary identifier of an event source. The analysis determines the characteristics of the attributes, which facilitates development of the database schema with availability, accuracy, existence, and other factors of various attributes. Analysis is supported for various formats of events, such as AVRO, XML, complex JSON, etc. In some examples, the attributes of interest for database schema generation can be provided via a configuration for the respective databases including relational, time-series, analytical, graph, etc. Also, if a given database supports direct ingestion of data through the message broker, then the ingestion specification can be generated.

Systems, apparatuses, and methods related to using memory device sensors are described. Some memory system or device types include sensors embedded in their circuitry. For instance, another device can be coupled to a memory device with an embedded sensor. The memory device can transmit a signal representing sensor data generated by the embedded sensor using a sensor output coupled to the other device. A controller coupled to a memory device may determine one or more threshold values of a sensor or sensors embedded in a memory device. The memory device may transmit an indication responsive to one or more sensors detecting a value greater or less than a threshold and may transmit the indication to another device.

An embodiment of the present disclosure provides a method and an apparatus for automatic operational maintenance. The method is executed on a cloud, and includes: in response to determining that an operational maintenance task in a preset task list satisfies an execution condition, issuing an operational maintenance instruction corresponding to the operational maintenance task to a remote device; receiving an execution result in response to the operational maintenance instruction returned by the remote device.