Techniques described herein relate to a method for using pipeline data for deploying workflows. The method may include determining that a pipeline testing trigger occurred for a workflow; decomposing a pipeline testing manifest of the workflow; generating a testing execution plan using the decomposed workflow; adding instrumentation to the testing execution plan; determining that the instrumented testing execution plan is valid; deploying computing devices within a CI/CD pipeline ecosystem for performing the instrumented testing execution plan; capturing deployment logs; initiating telemetry capture; executing pipeline testing of the workflow based on the instrumented testing execution plan; generating a pipeline deployment information set based on the pipeline testing; and providing the pipeline deployment information set to an orchestrator of a production device ecosystem.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for executing efficient and reliable techniques for testing application programming interfaces (APIs) by utilizing at least one of API endpoint modeling data entities and workflow design user interfaces that are generated based at least in part on API endpoint modeling data entities.

A homomorphic encryption-based testing computing system provides a risk-based, automated, one-directional push of production data through a homomorphic encryption tool and distributes the encrypted data to use in testing of applications. Data elements and test requirements are considered when automatically selecting a homomorphic encryption algorithm. A decisioning component selects an algorithm to use to homomorphically encrypt the data set and a push mechanism performs one or both of the homomorphic encryption and distribution of the encrypted data set to at least one intended host. Once delivered, the testing software and/or testing procedures proceed using the encrypted data set, where results of the testing may be stored in a data store. A validation mechanism may validate the test data against production data and communicates whether testing was successful.

This document describes a framework for measuring and improving the performance of applications, such as distributed applications and web applications. In one aspect, a method includes performing a test on an application. The test includes executing the application on one or more computers and, while executing the application, simulating a set of workload scenarios for which performance of the application is measured during the test. While performing the test, a set of performance metrics that indicate performance of individual components involved in executing the application during the test is obtained. A knowledge graph is queried using the set of performance metrics. The knowledge graph links the individual components to corresponding performance metrics and defines a set of hotspot conditions that are each based on one or more of the corresponding performance metrics for the individual components. A given hotspot condition is detected based on the set of performance metrics.

One example method includes deploying an application in a distributed computing environment. Telemetry data is collected that corresponds with the deployment of an application. The telemetry data is received by a machine learning model that was trained with test telemetry data to determine whether the deploying is successful or failed. A successful inference results in continued deployment and a fail inference results in a rollback of the application.

Comparing the performance of multiple versions or branches/paths of an application (e.g., a web service or application) may be conducted within a suitable computing environment. Such an environment may be virtual in nature, cloud-based, or server-based, and is hosted with tools for simultaneously (or nearly simultaneously) executing multiple containers or other code collections with the same or similar operating conditions (e.g., network congestion, resource contention, memory management schemes). By arranging the performance test of different application versions in different sequences executed in parallel in separate containers, fair comparisons of the tested applications will be obtained. Testing sequences may be executed multiple times, and metrics are collected during each execution. Afterward, the results for each metric for each code version are aggregated and displayed to indicate their relative performance quantitatively and/or qualitatively.

A system can identify a file comprising computer-executable instructions, wherein the file has been modified since the file was last transformed into a computer-executable program on which a group of tests was performed. The system can, for respective tests, determine respective line coverage ratios, respective function coverage ratios, and respective branch coverage ratios. The system can select an updated group of tests from the group of tests based on the respective line ratios, the respective function ratios, and the respective branch ratios, the updated group of tests comprising a subgroup of the group of tests. The system can create an updated computer-executable program from the file. The system can test the updated computer-executable program with the updated group of tests.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for executing efficient and reliable techniques for testing application programming interfaces (APIs) by utilizing at least one of API endpoint modeling data entities and workflow design user interfaces that are generated based at least in part on API endpoint modeling data entities.

Described are methods and systems for predicting and generating impacted scenarios based on a defined set of attributes. The system includes one or more databases. The processors are configured to receive a set of service provider system attributes for a project, generate attribute combinations from the set of service provider system attributes using a machine learning model trained on a reference data model, wherein the reference data model includes multiple test scenarios from the one or more databases, each test scenario associated with a test scenario attribute combination, generate predicted scenarios from the attribute combinations using the machine learning model, determine impacted service provider systems based on the predicted scenarios, determine issues based on each of the predicted scenarios, and generate a complexity score based on the determined impacted service provider systems and the determined issues to determine project viability.

Devices, systems, and methods for upgrading fire panel firmware using machine learning are described herein. In some examples, one or more embodiments include a fire panel comprising a processor and a memory having instructions stored thereon which, when executed by the processor, cause the processor to collect operational data associated with a fire system controlled by the fire panel device over a period of time while the fire panel device utilizes a first firmware version, determine a plurality of test cases based on the operational data, execute the plurality of test cases while the fire panel device utilizes a second firmware version, and provide results of each of the plurality of executed test cases via an interface.