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.

Application debug protocols that require waiting for responses between each request may be adversely affected if significant latency exists between a test device executing an application and a remote device used to debug the application. To address this, the test device is connected to a separate device that receives requests from the remote device. When a first request is received, the separate device determines other requests that are related to the first request, sequentially sends the other requests to the test device, and receives a response after each request, using a wired connection affected by less latency than communication with the remote device. The separate device then sends each of the requests and responses to the remote device for storage. When the remote device prepares to send a subsequent request, if a response can be determined using the stored data, the stored data is used to determine the response locally.

An analysis function imparting device (10) includes a virtual machine analyzing unit (121) that analyzes a virtual machine of a script engine, a command set architecture analyzing unit (122) that analyzes a command set architecture that is a command system of the virtual machine, and an analysis function imparting unit (123) that performs hooking for imparting multipath execution functions to the script engine, on the basis of architecture information acquired by the analysis performed by the virtual machine analyzing unit (121) and the command set architecture analyzing unit (122).

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 computer-implemented method for static testing a software system that is decomposed into software units connected by interfaces. The method comprises receiving context information for an interface, which includes at least one postcondition for the at least one output variable of a respective first software unit and/or a precondition for the input variable of a respective second software unit; receiving a selection of a third software unit in so that a substitute decomposition appertaining thereto of the software system into the third software unit and a complement of the third software unit is produced, the third software unit and the complement forming the software system and being connected via a substitute interface; selecting, based on the item of context information a postcondition per output variable of the complement; and testing whether the selected postcondition can be forward-propagated by the third software unit with regard to a formal verification.

A system for testing control units via simulation includes: a simulator; a host computer; and at least one connection for a communication system. At least one communication tool is stored on the system. Real control units are connectable to the system via the communication system. At least one controller is provided on the system for the connection to the communication system. Driver software for the at least one controller is stored on the system. The at least one communication tool is configured to generate communication code for communication between simulated control units and/or the real control units, wherein the communication code is configured to interact with the driver software and to relay signals and/or messages from the real and simulated control units to the driver software and to receive the signals and/or messages from the driver software. A loop mode is provided for the driver software.

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.