Described herein is a system and method for detecting correlated changes (e.g., between code files and configuration files). For a plurality of code files and a plurality of configuration files, a correlated change model is trained to identify correlated changes across the code files and the configuration files using a machine learning algorithm that discovers change rules using a support parameter, and, a confidence parameter, and, a refinement algorithm that refines the discovered change rules. The correlated change model comprising the change rules is stored. The correlated change model can be used to identify potential issue(s) regarding a particular file (e.g., changed code or configuration file(s)). Information regarding the identified potential issue(s) can be provided to a user.

A method for instrumenting an update to a software application may include determining, based on a source code file affected by the update to the software application, a first method affected by the update to the software application. A second method called by the first method and a third method called by the second method may also be identified as being affected by the update to the software application. A user interface file that includes a call to the first method, the second method, and/or the third method may be identified. The functional impact of the update may be determined by identifying one or more functional flows that match the user interface file. A recommendation identifying the one or more matching functional flows as candidates for testing may be generated. Related systems and computer program products are also provided.

In one embodiment, a computing device includes a memory device storing instructions, and a processing device communicatively coupled to the memory device. The processing device executes the instructions to execute a computer-implemented system configured to manage items in a self-describing data system, wherein the items are associated with a user interface and the computer-implemented system is associated with a first version; execute a third-party computer-implemented system configured to execute an application that accesses the user interface, wherein the third-party computer-implemented system is associated with a second version; and execute a test automation framework (TAF) configured to use the third-party computer-implemented system to perform tests on the user interface associated with the items. The TAF comprises libraries including code specific to the first version of the computer-implemented system and code specific to the second version of the third-party computer-implemented system.

A program development assistance system includes an automatic execution process server that accepts a commit completion notification indicating that a source code has been registered, commit information that includes source code information and ticket information is acquired, a ticket identifier is extracted from the commit information, attribute information pertaining to a ticket is acquired on the basis of the extracted ticket identifier, information to be executed that corresponds to the source code to be processed by automatic execution is stored in an automatic execution queue, the sequence of to-be-executed information in the automatic execution queue is altered on the basis of the acquired attribute information pertaining to the ticket, the source code and a test case that are to be processed by automatic execution are acquired, and an automatic execution process is performed using the source code and the test case on the basis of the sequence of to-be-executed information.

Mutation testing can indicate whether mutants of a software application, created by intentionally altering source code of the software application, are successfully “killed” by test cases executed against the mutants. Mutation testing can be performed via parallel threads by, within each parallel thread, modifying individual source code class files and recompiling the modified class files to generate and test mutants. Individual mutation test results produced within each of the parallel threads can be aggregated to generate an aggregated test result report that indicates overall testing metrics associated with the mutation testing across the parallel threads.

Systems and methods for automated test data microservices are provided. Test versions of software (such as an Application Programming Interface (API)) may be configured to automatically generate test data and to call a microservice to manage the test data. The microservice may automatically add and remove the test data from an operational data store to facilitate the testing process and to automatically perform setup and teardown stages of the testing process.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: onboarding data received from one or more entities to create a version of an artifact of a first distributed architecture; periodically running one or more test cases using the version of the artifact in an environment; detecting a modification to the version of the artifact; automatically generating a modified version of the artifact incorporating the modification; selecting a first artifact from the list of one or more artifacts associated with the first distributed architecture, wherein the first artifact comprises an internet protocol (IP) address; deploying the first artifact, by using the IP address, into a networking sandbox to implement changes to the first artifact corresponding to a particular objective; and building a second artifact. Other embodiments are disclosed.

Methods, systems, and computer-readable media for GPU code injection to summarize machine learning training data are disclosed. Training of a machine learning model is initiated using a graphics processing unit (GPU) associated with a machine learning training cluster. The training of the machine learning model generates tensor data in a memory of the GPU. The GPU determines a summary of the tensor data according to a reduction operator. The summary is smaller in size than the tensor data and is output by the GPU. A machine learning analysis system performs an analysis of the training of the machine learning model based at least in part on the summary of the tensor data. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis.

A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process that includes acquiring first information indicating a difference between display elements in a first screen of first software before a test operation indicated by a test case is performed for the first screen and display elements in a second screen of the first software after the test operation is performed for the first screen, acquiring second information indicating a difference between display elements in a third screen of second software before the test operation is performed for the third screen and display elements in a fourth screen of the second software after the test operation is performed for the third screen, the second software being generated by updating the first software, and determining whether there is compatibility of the test case between the first and second software based on the first and second information.

A method, system, and computer program product for testing a container orchestration system are disclosed. The method includes replicating objects of a production cluster by extracting an object definition from an object and transforming the object definition to create a replicated object definition with an equivalent syntactic form. The replicated object definition requires fewer resources than the object definition. The method also includes applying the replicated objects of the production cluster to a simplified test cluster that replicates a configuration of the production cluster in a scaled down form. Additionally, the method includes testing, with the simplified test cluster, an upgraded version of the container orchestration system.