Deterministic replay of events between software entities. In current frameworks, replays of events (e.g., data communications) between software entities are non-deterministic and unreproducible. In an embodiment, as events are replayed, software entities, stimulated by those events, are queued according to a queuing strategy and executed from the queue. In an alternative embodiment, as software entities are executed, the events, output by those software entities, are queued according to a queuing strategy and played from the queue. Both embodiments ensure determinism and reproducibility across replays.

Methods, systems, and computer-readable storage media for receiving a capture file, the capture file including data representative of a workload executed in a source database system, and processing the capture file to provide a replay file, the replay file being in a format that is executable by a replayer to replay the workload in a target database system, processing the capture file including: processing the capture file to provide a set of intermediate files, and processing the set of intermediate files using in-memory merge sort operations to generate a set of replay files.

A method of preparing an input vector for a neural network includes capturing a plurality of information about a storage system, including workload types, a processing graph, and read/write histograms, and creating a correlation matrix from processing times of different levels of processes in a workload of the storage system. The input vector is prepared with a workload vector representing the workload types, a behavior matrix representing the processing graph, a read/write histogram shape matrix representing the read/write histograms, and the correlation matrix.

System and method are provided for facilitating performance testing. The method includes mapping source code for an application under test to at least one performance test script, the at least one performance test script for executing functions of the application; determining at least one source code change from build release notes; receiving production environment data for the application; using the build release notes and the mapped source code to identify at least one functionality of the application associated with the at least one source code change; for each identified functionality, analyzing corresponding production environment data for a period of time; generating, based on the analysis, a trained workload model for the identified at least one functionality, the trained workload model providing a framework for subsequent performance testing of the application; determining one more performance tests based on the trained workload model; and automatically initiating at least one of the one or more performance tests using the trained workload model.

A method includes receiving, by a computing device, a Structured Query Language (SQL) query from a user; generating, by the computing device, execution structures from the SQL query; generating, by the computing device, test results by running the SQL query with the execution structures; building, by the computing device, logs which record information of the running of the SQL query; generating, by the computing device, a candidate execution structure using the information from the logs; normalizing, by the computing device, the SQL query using the candidate execution structure; running, by the computing device, the normalized SQL query in a database; and comparing, by the computing device, results of the normalized SQL query to the test results.

In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Disclosed herein are system, method, and computer-readable medium embodiments for providing the ability to automate the process of generating load tests used for benchmarking APIs. Rather than having to manually generate load tests for a web service API, a test developer can interact with a test service through a web browser and provide the service an API specification and testing parameters. The test service can analyze the API specification, automatically identified endpoints of the API, and generate load tests according to the expected input/output structures of the endpoints. The automatic load test generation can proceed by referring to a library of test instructions and extracting the portions of the test instructions that work for the identified endpoints.

Techniques described herein relate to a method for optimizing workflow execution. The method may include receiving an event notification at a service controller, wherein the event notification is associated with a workflow deployed in a device ecosystem; performing, based on receiving the event notification, a workflow reconfiguration action comprising: providing a first workflow portion to a first platform controller in a first domain in the device ecosystem; transmitting a workflow reconfiguration action notification to a second platform controller in a second domain of the device ecosystem; identifying, by the second platform controller and in response to receiving the workflow reconfiguration action notification, a data transfer optimization action associated with data to be transmitted from the second domain to the first domain and used during execution of the first workflow portion; and transmitting the data from the second domain to the first domain using the data transfer optimization action.

A sample is analyzed to determine a set of events that should be selected for performing by a dynamic analyzer executing the sample in an instrumented, emulated environment. The set of selected events is performed. In some cases, at least one emulator detection resistance action is performed. A maliciousness verdict is determined for the sample based at least in part on one or more responses taken by the sample in response to the set of selected events being performed by the dynamic analyzer.

To obtain one or more recommendations for the migration of a database to a cloud computing system, information about performance of the database operating under a workload may be obtained. A first machine learning model (e.g., a neural network-based autoencoder) may be used to generate a compressed representation of characteristics of the database operating under the workload. The compressed representation may then be provided as input to a second machine learning model (e.g., a neural network-based classifier), which outputs a recommendation regarding a characteristic (e.g., size, configuration, level of service) of the cloud database to which the database should be migrated. This type of recommendation may be made prior to migration, thereby making it easier to properly estimate the cost of running the cloud database and plan the migration accordingly.