Quality indicators regarding a software project under development that has a plurality of functional areas are collected. Each quality indicator is normalized to a coverage of the functional area of the software project to which the quality indicator corresponds. The normalized quality indicators are correlated to previously identified anomalies of the software project, yielding an anomaly indicative value for each normalized quality indicator corresponding to a probability that the normalized quality indicator is revelatory of unidentified anomalies of the software project. A normal behavior for each normalized quality indicator is estimated. For each functional area of the software project, an anomaly score indicative of a likelihood of an unidentified anomaly within the functional area is calculated. The anomaly score is based on, for each normalized quality indicator corresponding to the functional area, how the normalized quality indicator departs from its estimated normal behavior as weighted by its anomaly indicative value.

An analysis system determines a system aspect of a system, determines an evaluation perspective for use in performing an asset management evaluation on the system aspect relating to a build of the system, an evaluation viewpoint corresponding to discovered information of the system and selects a plurality of data structures identifying data to be collected based thereupon. The analysis system, based upon the system aspect, the evaluation perspective, the evaluation viewpoint, and the plurality of data structures, determining context data. Based upon the plurality of data structures, the analysis system identifies a plurality of physical assets of the system for collection of data, queries the plurality of physical assets of the system to collect data to populate the plurality of data structures. The analysis system evaluates the data structures using the context data to produce an evaluation of at least some of the plurality of physical assets of the system.

A SaaS system and methods for capturing dataflow integration and optimizing continuity of operation are presented. Consistent with some embodiments, the method may include receiving a dataflow, and calculating a plurality of attribute scores for the dataflow. The method may further include causing a client device to automatically store a dataflow from the dataflow in response to determining that at least a portion of the plurality of attribute scores are above a predefined threshold. The method may further include receiving a dataflow from a recording application associated with a client device and providing to the user of the client device dataflow-recording directions which are adapted to predetermined criteria that correspond to the purpose of dataflow-recording, the type of activity to be presented in said dataflow.

A system and method are disclosed for creating solution design blueprints. A solution design blueprint is a machine-readable data structure that includes a conceptual design model for an application framework. A user interface is configured to receive a plain language textual request from a user that describes a desired application or solution to a problem. Artificial Intelligence is leveraged to fit the textual request to semantic data models and map elements of the textual request to components of a design library. The resulting solution design blueprint can be presented to a user, and the user interface can be used to provide feedback related to the solution design blueprint that can be utilized to update machine learning algorithms and/or neural networks. In some embodiments, the solution design blueprint can be converted to an application framework that is provided to the use in an integrated development environment of the user interface.

Systems and methods are disclosed for automatically assessing and conforming software development modules to accessibility guidelines in real-time. The systems may facilitate an incremental development of applications. One or more modules or base codes of the application, as they are developed, may be tested for compliance to various accessibility standards (e.g., Web Content Accessibility Guidelines 2.0). If a module or base does not meet a specific threshold of compliance, systems and methods allow for an automatic modification of the module or base code to make it more compliant to the accessibility standards.

An example method of operation may include automatically receiving information from a storage area in response to a signal, which information may include test results for a computer product evaluated by a plurality of test stages. The method may also include retrieving deployment parameters for the computer product, determining whether the test results satisfy the deployment parameters for the computer product, and automatically authorizing deployment of the computer product based on whether the test results satisfy the deployment parameters.

Techniques and apparatus for installation optimization process are described. In one embodiment, for example, an computing device may include at least one memory; at least one storage device; and logic coupled to the at least one memory to perform an installation process, the logic to: determine installation requirements for an analytical platform, the installation platform comprising at least one of an analytical application, a database application, and a fast recovery area (FRA), perform at least one prerequisite test based on a hardware configuration of the computing device and the installation requirements, and perform a drive optimization process to determine at least one optimal drive configuration for installation of the installation platform based on remaining free space (RFS) on the at least one storage device and at least one storage device installation rule. Other embodiments are described.

A system and method are disclosed for running a plurality of simulation tests on a first machine learning model to obtain a plurality of results that are each produced during a respective simulation test, the first machine learning model gradually trained using first training data historically collected over a period of time, the first training data comprising a plurality of first training data sets each including a subset of first training inputs and first target outputs associated with one of a plurality of points in time during the period of time, determining a simulation test of the plurality of simulation tests at which corresponding results of the first machine learning model satisfy a threshold condition, wherein the threshold condition is based on historical data at a first point in time of the plurality of points in time, identifying a first training data set of the plurality of first training data sets on which the first machine learning model used during the determined simulation test was trained, wherein the first training data set on which the first machine learning model used during the determined simulation test was trained is associated with one or more second points in time that precede the first point in time, and determining a subset of target outputs from the identified first training data set on which the first machine learning model used during the determined simulation test was trained, the determined subset of first target outputs to define an amount of second training data to be sufficient to train a second machine learning model.

Systems for testing computing devices of a medical records system using a simulated environment are provided. The simulated environment is generated by identifying transaction traffic over the network, where the transaction traffic comprises messages of different transaction types. A computing device is configured to output a simulated status by extrapolating a performance of the computing devices upon receiving a series of messages indicating the transaction types. The output of the computing device may indicate an error in the simulated environment where the performance of a computing device exceeds a performance threshold. Based on the error indication, transaction traffic can be diverted from the computing device to another computing device having performance capacity.

Methods and articles of manufacture for hosting a safety critical application on an uncontrolled data processing device are provided. Various combinations of installation, functional, host integrity, coexistence, interoperability, power management, and environment checks are performed at various times to determine if the safety critical application operates properly on the device. The operation of the SCA on the UDPD may be controlled accordingly.