Abstract: A tenant data store contains information associated with tenants of a cloud computing environment, including an indication that a first customer subscribed to an exclusive tenant that receives an upcoming version of runtime software. A notification system pushes a notification about the upcoming runtime to an integration service for the first customer. A tenant preview service may contain the upcoming version of runtime software, and a software update store may contain new integration scenario content (created and tested by the first customer) in connection with the upcoming version of runtime software. An integration content mapper determines, based on old and new integration scenario content metadata, if an automatic port of the new integration scenario content in the software update store should be automatically provided to a runtime node. If it should be automatically provided, the integration content mapper may use a software update tool to automatically deploy the new integration scenario content.

Abstract: In some implementations, a system may receive a first set of files output based on a machine learning model that is trained using training data that includes a set of values, for each of multiple observations, that corresponds to a set of features. The system may receive a second set of files that includes information that identifies a set of valid values corresponding to the set of features. The system may generate, based on the first set of files and the second set of files, code for generating the output score from the trained model based on a set of input values that are input to the trained model. The system may deploy the code for execution in a computing environment.

Abstract: 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.

Abstract: By analyzing a test case in a set of test cases, the test case is classified into a test type. Using a result of analyzing a test execution environment, a flake parameter is set, the flake parameter comprising an execution environment characteristic capable of causing an inconclusive result of execution of the test case. Responsive to determining that the test type maps to the flake parameter, the test case is removed from the set of test cases, the removing resulting in a filtered set of test cases, the determining performed using a predefined set of mappings. The filtered set of test cases is executed in the test execution environment.

Abstract: A method of building a software development project environment.

Abstract: 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.

Abstract: Methods, systems, and apparatus for propagating reduced-precision on computation graphs are described. In one aspect, a method includes receiving data specifying a directed graph that includes operators for a program. The operators include first operators that each represent a numerical operation performed on numerical values having a first level of precision and second operators that each represent a numerical operation performed on numerical values having a second level of precision. One or more downstream operators are identified for a first operator. A determination is made whether each downstream operator represents a numerical operation that is performed on input values having the second level of precision. Whenever each downstream operator represents a numerical operation that is performed on input values having the second level of precision, a precision of numerical values output by the operation represented by the first operator is adjusted to the second level of precision.

Abstract: 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.

Abstract: An electronic system includes a first data retriever to communicate with a first database associated with an analytic tool, and a second data retriever to communicate with a second database associated with an electronic testing device; wherein the analytic tool is configured to store information regarding actual usage of a product in the first database; wherein the electronic testing device is configured to store product testing data for the product in the second database; wherein the first data retriever comprises a first communication interface to electronically receive the information; wherein the second data retriever comprises a second communication interface to electronically receive the product testing data; and wherein the electronic system further comprises a processing unit configured to compute a score indicating a quality of product testing for the product based on the information regarding the actual usage of the product and/or the product testing data.

Abstract: Disclosed herein are systems and methods for multi-system connectivity and automation via universal computing elements. Universal computing elements may comprise an object queue, one or more counters, and a function operating on parameters of objects in the object queue. Universal computing elements may be interconnected into processes of arbitrary complexity. Universal computing elements may facilitate modular and scalable business process development, including application programming interface and database connectivity.

Abstract: Training and/or utilization of machine learning model(s) (e.g., neural network model(s)) in automatically generating test case(s) for source code. Techniques disclosed herein can be utilized in generating test case(s) for unit test testing (or other white-box testing) and/or for functional testing (or other black-box testing). In some implementations, the machine learning model(s) can be trained on source code, unit test pairs. In some additional or alternative implementations, reinforcement learning techniques can be utilized to check for correctness of base source code, target source code pairs (e.g., by matching program execution of different branches).

Abstract: Methods, apparatus, and processor-readable storage media for implementing extensible upgrade and modification as a service are provided herein. An example computer-implemented method includes processing one or more modifiers, wherein each modifier includes an independent processing unit having a given canonical structure and is configured to execute one or more automated actions related to at least one of application modification and application migration; obtaining data pertaining to multiple applications across multiple computing environments; determining, based at least in part on processing at least a portion of the obtained data, at least one of the one or more modifiers applicable for use in executing at least one of the one or more automated actions in connection with at least a portion of the multiple applications; and executing the at least one of the one or more automated actions using the at least one determined modifier.

Abstract: A method of guaranteeing game quality by using an artificial intelligence (AI) agent is provided. The method includes extracting an item list (hereinafter referred to as an inspection item list) for inspecting quality of a target game, extracting and storing log data corresponding to a test performance result for each item of the inspection item list, performing imitation learning of an AI agent model on the basis of the stored log data, performing an automatic test for inspecting quality of the target game by using the AI agent model on which the imitation learning is completed, and automatically recording a bug and an error detected by the AI agent model.

Abstract: A method for optimizing a verification regression includes obtaining data, by a processor, of previously executed runs of at least one verification regression session; extracting from the data, by the processor, values of one or a plurality of control knobs and values of one or a plurality verification metrics that were recorded during the execution for each of the previously executed runs of said at least one verification regression; finding, by the processor, correlation between said one or a plurality of the control knobs and each said one or a plurality of verification metrics, and generating a set of one or a plurality of control conditions based on the found correlation; and applying, by the processor, the generated set of one or a plurality of control conditions on the verification environment or on the DUT, or on both, to obtain a new verification regression session.

Abstract: A method, system, and computer program product to aggregate code change requests for a continuous integration pipeline. The method may include receiving a plurality of code change requests (CCRs) for a software product that are to be processed by a continuous integration (CI) pipeline. The method may also include obtaining all functions of the software product tested by all testcases in a testcase set. The method may also include selecting a plurality of groups of testcases from the testcase set, each group of the plurality of groups of testcases corresponding to a CCR from the plurality of CCRs. The method may also include identifying at least two groups of testcases from the plurality of groups of testcases without overlapping code. The method may also include aggregating each CCR that corresponds to the at least two groups of testcases into a single CCR group.

Abstract: Systems and methods are disclosed for dynamic application mobilization. In one implementation, an application is modeled as a processing function. The processing function is placed at a first processing node within a processing network. One or more optimization operations are computed with respect to the processing function. The processing function is mobilized from the first processing node to a second processing node within the processing network in accordance with the one or more optimization operations.

Abstract: Implementations generally relate to software testing system and method for automatically generating, deploying, and monitoring software tests employed to test software applications and/or software process flows. In implementations, a system generates software application tests and varies such tests used based on several criteria including monitoring a software application processing defined inputs and outputs to determine which parts of the application are being tested, which parts of the software application are not being tested, user interactions, metadata, type of software application, use computing environment, etc. The system may be configured to adapt testing, either pre or post production, to allow, user interactions of one or more running software applications to be used to provide feedback on which additional tests to add and/or subtract automatically to testing sequences.

Abstract: A method of compiling a software application as modular software build, comprising: creating a dependency tree defining a plurality of dependency relations between a plurality of code segments of a software code of a software application, each one of the plurality of dependency relations defines at least one of a functionality dependency, a parameter dependency and a structure dependency between a pair of the plurality of code segments; separately compiling each of the a plurality of code segments into one of a plurality of executable segment modules; and creating a core module adapted to be installed on a client device having a processor for loading the plurality of executable segment modules according to the plurality of dependency relations for an execution of the software application.

Abstract: Machine learning techniques are employed to model test runs of an automated test platform in ways that allow for reliable identification of various types of test behavior such as, for example, whether certain classes of failures can be characterized as test flake.

Abstract: A system and method for automation of task identification and control in a software lifecycle. Software context for a software asset is extracted from context repositories of the software asset during software development and operation, the extracted context data is matched to relevant tasks in a knowledge database to select tasks for the software asset, and task prioritization and orchestration are presented in a prioritized task list during a software lifecycle.