Systems for web page or web application instrumentation. Embodiments commence upon identification of a computer-readable user interface description comprising at least some markup language conforming to a respective document object model that is codified in a computer-readable language. An injector process modifies the user interface description by inserting markup text and code into the user interface description, where the inserted code includes instrumentation code to invoke dynamic generation of instrumentation locator IDs using the hierarchical elements found in the document object model. The modified computer-readable interface description is transmitted to a user device. Log messages are emitted upon user actions taken while using the user device. The log messages comprise the instrumentation locator IDs that are formed using hierarchical elements found in the document object model.

A technique for tracking web browsing activity of a client device that includes storing, in a memory, a client profile having a client identifier associated therewith, providing a client device with a cache file having the client identifier embedded therein, receiving from the client device an identification of a client action and the client identifier, and updating the client profile to include the identification of the client action.

A plurality of completion times associated with an application request may be obtained. The plurality of completion times may include a first completion time and a second completion time. A plurality of response times associated with a first asynchronous operation triggered by the application request may be obtained. The plurality of completion times may include a first response time associated with the first completion time and a second response time associated with the second completion time. A first correlation score may be determined describing an effect of the first asynchronous operation on the application request based on the first completion time, the second completion time, the first response time, and the second response time. Visualization data may be generated representing the first correlation score.

A computer-implemented method includes scanning a clip of messages that includes message requests and message responses arranged in a sequence. The scanning is performed based on one or more search parameters and produces a list of one or more name/value pairs. The clip is utilized to perform a load test on a target website. Each name/value pair has a corresponding value. For each name/value pair in the list a message request in the clip is identified where the corresponding value is first used. Then, looking backwards in the sequence from the message request where the corresponding value is first used, prior message responses are located where the corresponding value is found. An extraction point is specified in the clip for the corresponding value as a latest message response in the sequence where the corresponding value was returned from the target website. The corresponding value is then stored as a property.

Techniques for identifying and remedying performance issues of Virtualized Network Functions (VNFs) are discussed. An example method includes outputting a request to a network Element Manager (EM) to create a Virtualized Network Function (VNF) Performance Measurement (PM) job to collect VNF PM data from a VNF and receiving a set of VNF PM data associated with the VNF from the EM. The set of VNF PM data is processed associated with the VNF. A request to the EM is output to create a Virtualization Resource (VR) PM job to collect, through a VNF Manager (VNFM) and a virtualized infrastructure manager (VIM), VR PM data from a VR used by the VNF. Then a set of VR PM data is received from the EM and processed.

Methods and apparatus are described by which a rich, time-correlated information set is captured during automated testing of an application in a way that allows the application developer to understand the state of the application under test (AUT), the browser interacting with the AUT, and/or the device interacting with the AUT, as it/they changed over time. Mechanisms or features associated with browsers and/or device operating systems are exploited to capture such information, not only for the purpose of better understanding individual test runs, but also to enable the use of analytics over data sets.

The data collection management device (10) is connected via a network to a plurality of communication devices (20) performing cyclic communication and includes: a network configuration storage (17) to store network configuration information indicating the communication devices participating in the cyclic communication; a data receiving unit (11) to receive communication data multicast from each communication device (20); a received data storage (12) to store the received communication data as collected data; a received data determination unit (13) to determine whether there is missing data in the collected data and identify unreceived communication data, based on information specifying communication cycles included in the collected data, on information specifying sender communication devices included in the collected data, and on network configuration information; and a retransmission requesting unit (15) to transmit a retransmission request of the unreceived communication data to one of the plurality of communication devices (20).

Load testing a service having a plurality of different states is provided. A multitude of simulated users accessing the service are divided into a plurality of cohorts. Simulated users within a given cohort share a similar personality type. A load test of the service is performed by applying a set of service requests from each respective cohort to the service. In response to a percentage of simulated users of each cohort encountering a particular state in the service, a user response is determined for the percentage of simulated users within each cohort at that particular state based on a probabilistic user behavior model corresponding to a personality type of each cohort such that user responses at that particular state are distributed in accordance with the probabilistic user behavior model. Distributed user responses at that particular state are applied to the load test in accordance with the probabilistic user behavior model.

Techniques for enabling secure access to data using data blocks is described. Computing device(s) can provide instruction(s) to a component associated with an entity, wherein the instruction(s) are associated with an identifier corresponding to a data block of a plurality of data blocks. The computing device(s) can receive, from the component, data associated with the component, wherein the data is associated with the identifier and is indicative of a state of the component. The computing device(s) can store the data in the data block and monitor, using rule(s), changes to the state of the component based at least partly on the data in the data block. As a result, techniques described herein enable near real-time—and in some examples, automatic—reporting and/or remediation for correcting changes to the state of the component using data that is securely accessed by use of data blocks.

Back end calls triggered by a user interaction with a client user interface may be identified. The user interaction may be correlated with a logic flow, and the logic flow may be associated with the back end calls. A supervised learning model may be trained using a labeled data set comprising the back end calls and their associated logic flow. Rules may be derived from the supervised learning model for classifying other back end calls. The rules may be outputted to a classifier that utilizes the rules to associate the other back end calls with the logic flow.