Aspects of this disclosure relate to using artificial intelligence (“AI”) to control integration of software developed by a third-party into an enterprise computing environment subject to more rigorous regulatory and security testing than typically provided by the third-party. AI software development automation tools will deploy third-party scripts to edge servers. Deploying to edge servers allows for integration of the third-party tags into testing environment pipelines. Local storage associated with third-party tags will be at a top-level domain, allowing third-party software tags to be treated as first party without the reputational and technical risks of cross-site storage.

A data exchange that provides self-service data provisioning is provided. The data exchange may include a raw data layer, a model data layer, a plurality of workspaces and a testing environment. The raw data layer may be a landing zone for raw data records received from systems of record. The raw data layer may receive a plurality of raw data records, model and process the data records and transfer the data records to the model data layer. The model data layer may be a data layer that includes data modeled to data exchange specifications and enables queries to be executed on the data included in the model data layer. Each workspace may be allocated to a consumer. The consumer may query the plurality of data records within the model data layer. The testing environment may test scripts to ensure that the scripts conform to a predetermined set of testing specifications.

The systems and methods that determine tests that may be executed in parallel during regression testing of an analytics application are provided. Multiple tests that test functions of the analytics application are accessed from a test automation suite. For each test, data sources that provide data to the analytics application during the test are identified. The tests are aggregated into temporary groups according to the identified data sources. The test groups are generated from the temporary groups such that each test group comprises tests that are associated with non-overlapping data sources. The regression testing is performed on the application by executing the test groups in parallel.

Systems, methods, and machine-readable instructions stored on machine-readable media are disclosed for adjusting a time limit for a test based on one or more indications of availability. A test is executed, wherein the test includes a time limit. A determination is made that the time limit is exceeded. In response, the time limit is adjusted based on one or more indications of availability.

Operations may include obtaining a plurality of posts from one or more web sites, each post including a respective buggy snippet of source code that includes a corresponding error. The operations may also include generating a plurality of bug patterns from the plurality of posts in which each respective bug pattern corresponds to a respective buggy snippet and indicates a corresponding bug scenario that leads to the corresponding error of the respective buggy snippet that corresponds to the respective bug pattern. The operations may also include determining similarities with respect to the respective bug patterns and selecting, based on the similarity determinations, a first bug pattern of the plurality of bug patterns for revision. In addition, the operations may include obtaining a revised bug pattern that is a revised version of the first bug pattern.

A system for checking security vulnerabilities for automation system design includes a security database, an Internet crawler application, and security service application. The security database stores descriptions of known software vulnerabilities related to an automation system. The Internet crawler application is configured to systematically browse the Internet to find new software vulnerabilities related to the automation system and index the new software vulnerability into the security database. The security service application retrieves, from the security database, potential software vulnerabilities related to a hardware/software configuration of the automation system. The security service application also identifies policies related to the potential vulnerabilities. Each policy describes a potential vulnerability and action to be performed in response to detection of the potential vulnerabilities. The security service applies the policies to the hardware/software configuration and software code corresponding to an automation application to identify actual vulnerabilities that can be displayed to a user.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

A method, an improvement node, a system and a computer program for computing an improvement result for a runtime environment of at least one application, on a device in a medical context. An embodiment of the method includes detecting a state of the runtime environment on the device; accessing a database with the state detected, to retrieve a corresponding at least one candidate improvement result; using the at least one candidate improvement result retrieved, for test-wise execution on a test infrastructure in which the state of the runtime environment detected is provided identically; measuring improvement parameters of the test-wise execution; and adding, upon the improvement parameters measured meeting defined requirements, candidate improvement results, of the at least one corresponding candidate improvement result retrieved, for which the improvement parameters measured meet defined requirements.

A method and system for generating an audit report is described. Structured data that represents a prior performance of a business process is received at a processor from a staging database configured to receive raw data from a plurality of distinct data sources. Test control functions are selected by the processor from a plurality of predetermined test control functions. The plurality of predetermined test control functions are configured to read structured data from the staging database and to process the structured data to determine whether business processes have been properly performed. The selected one or more test control functions are executed by the processor to determine whether the business process has been properly performed using the received structured data. The audit report is generated by the processor to include the determination by the selected one or more test control functions of whether the business process has been properly performed.

Techniques are disclosed relating to automated operations management. In various embodiments, a computer system accesses operational information that defines commands for an operational scenario and accesses blueprints that describe operational entities in a target computer environment related to the operational scenario. The computer system implements the operational scenario for the target computer environment. The implementing may include executing a hierarchy of controller modules that include an orchestrator controller module at top level of the hierarchy that is executable to carry out the commands by issuing instructions to controller modules at a next level. The controller modules may be executable to manage the operational entities according to the blueprints to complete the operational scenario. In various embodiments, the computer system includes additional features such as an application programming interface (API), a remote routing engine, a workflow engine, a reasoning engine, a security engine, and a testing engine.