Systems and methods for remediation of software configurations are disclosed. The system may store a plurality of configuration policies in a compliance repository. The system may receive trigger data including at least one compliance error and indicating a software instance operating on a cloud service is out of compliance. The system may compare the at least one compliance error with the plurality of configuration policies. When at least one compliance error matches at least one configuration policy, the system may identify a software configuration file and apply the matching configuration policy to the software configuration file to remediate the software instance. When the at least one compliance error does not match at least one configuration policy, the system may generate a new configuration policy, validate the new configuration policy, and apply the new configuration policy to the software configuration file to remediate the software instance.

A method of processing test execution logs to determine error location and source includes creating a set of training examples based on previously processed test execution logs, clustering the training examples into a set of clusters using an unsupervised learning process, and using training examples of each cluster to train a respective supervised learning process to label data where each generated cluster is used as a class/label to identify the type of errors in the test execution log. The labeled data is then processed by supervised learning processes, specifically a classification algorithm. Once the classification model is built it is used to predict the type of the errors in future/unseen test execution logs. In some embodiments, the unsupervised learning process is a density-based spatial clustering of applications with noise clustering application, and the supervised learning processes are random forest deep neural networks.

A service testing system is disclosed to enable consistent replay of stateful requests on a service whose output depends on the service's execution state prior to the requests. In embodiments, the service implements a compute engine that executes service requests and a storage subsystem that maintains execution states during the execution of stateful requests. When a stateful request is received during testing, the storage subsystem creates an in-memory test copy of the execution state to support execution of the request, and provides the test copy to the compute engine. In embodiments, the storage subsystem will create a separate instance of execution state for each individual test run. The disclosed techniques enable mock execution states to be easily created for testing of stateful requests, in a manner that is transparent to the compute engine and does not impact production execution data maintained by the service.

Systems and methods are provided to recommend software patches based on task operation mapping. In embodiments, a method includes abstracting test cases for a software patch into a sequence of task operations and parameters associated with each task operation; encoding the task operations and the parameters associated with each task operation based on predetermined rules, thereby generating encoded task operations with unique identifiers assigned thereto and associated encoded parameters with numeric values assigned thereto; generating, using machine learning, a list of frequent operation items, based on the encoded task operations and the associated encoded parameters; generating, using clustering, clusters of parameters for each frequent operation item in the list of frequent operation items; and sending a software patch package including the list of frequent operation items, the clusters of parameters and the software patch to a remote server for distribution to one or more user devices.

An automated fault injection testing and analysis approach drives fault injection into a processor driven instruction sequence to quantify and define susceptibility to external fault injections for manipulating instruction execution and control flow of a set of computer instructions. A fault injection such as a voltage or electromagnetic pulse directed at predetermined locations on a processor (Central Processing Unit, or CPU) alters a result of a processor instruction to change values or execution paths. One or more quantified injections define an injection chain that causes a predictable or repeatable deviant result from an expected execution path through the code executed by the processor. Based on accumulation of fault injections and results, a repeatable injection chain and probability identifies an external action taken on a processing device to cause unexpected results that differ from an expected execution of a program or set of computer instructions.

A method including: storing, in a memory, a test database, the test database including a plurality of test definitions, each test definition being associated with a respective base application feature and a respective destination application feature; detecting a request to generate a testing plan; generating the testing plan in response to the request, the testing plan being generated by using the test database, the testing plan identifying a sequence of at least some of the test definitions that are part of the test database; and outputting an indication of the testing plan for presentation to a user.

A robot design interface comprises tools for testing a robotic process automation (RPA) workflow. Some embodiments automatically generate a mock workflow comprising a duplicate of the original workflow wherein a set of RPA activities are replaced with substitute activities for testing purposes. Some embodiments expose an intuitive interface co-displaying the substitute activities in parallel to their respective original activities and enabling a user to configure various mock parameters. Testing is then carried out on the mock workflow.

A computer-implemented method is provided for data augmentation. The method includes receiving a set of different base models already pretrained and a set of different test cases. The method further includes collecting a plurality of prediction results of the set of different test cases from the set of different base models. The method also includes identifying a test case as a candidate for the data augmentation based on a number of models in the set of different base models which fail to solve the test case. The method additionally includes augmenting, by a processor device, the identified test case with additional data to form an augmented training dataset. The method further includes retraining at least some of the different base models with the augmented training dataset.

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.