According to some embodiments, methods and systems may be associated with trustworthy application integration. A formalization platform may facilitate definition of pattern requirements by an integration developer. The formalization platform may also formalize singe pattern compositions and compose single patterns to template-based formalized compositions. A correctness platform may then check for structural correctness of the formalized compositions and execute a semantic transformation or binding to pattern characteristics and associated interactions. The correctness platform may also check composition semantics and generate a formal model. An implementation platform may translate the formal model generated by the correctness platform and configure implementation parameters of the translated formal model. The implementation platform may then execute the translated formal model in accordance with the configured implementation parameters.

According to some embodiments, methods and systems may be associated with trustworthy application integration. A formalization platform may facilitate definition of pattern requirements by an integration developer. The formalization platform may also formalize singe pattern compositions and compose single patterns to template-based formalized compositions. A correctness platform may then check for structural correctness of the formalized compositions and execute a semantic transformation or binding to pattern characteristics and associated interactions. The correctness platform may also check composition semantics and generate a formal model. An implementation platform may translate the formal model generated by the correctness platform and configure implementation parameters of the translated formal model. The implementation platform may then execute the translated formal model in accordance with the configured implementation parameters.

A processor-implemented method is disclosed. The method includes: obtaining, from an activity logging system, activity data associated with one or more defined computing tasks, the activity data indicating progress towards completion of the one or more defined computing tasks, the defined computing tasks being associated with one or more projects; obtaining, from a resource usage monitoring system, time-based resource tracking data associated with at least one of the projects, the resource tracking data including project identifying data associated with the at least one project and project time data identifying one or more time periods reflecting use of a computing resource in association with the at least one project; determining mappings of the one or more time periods to the one or more defined computing tasks based on the project identifying data and the activity data associated with the one or more defined computing tasks; determining, based on the mappings, that at least one task-based resource usage criterion is satisfied; and in response to determining that the at least one task-based resource usage criterion is satisfied, generating a notification of resource usage for display on a computing device.

Methods, apparatuses, systems, and computer program products are disclosed for outputting a contextually relevant development unit performance insight interface component in a project management and collaboration system. In an example embodiment, an apparatus detects an insight interface component request, accesses past development unit performance metrics data, determines a suggested development unit performance target, determines a selected development unit commitment, determines a visual emphasis element for the selected development unit commitment, wherein the visual emphasis element is configured to visually compare the selected development unit commitment to the suggested development unit performance target, generates a development unit performance summary insight interface component comprising the visual emphasis element, and outputs the development unit performance summary insight interface component for rendering to a project management user interface.

Methods, apparatuses, systems, and computer program products are disclosed for outputting a contextually relevant development unit performance insight interface component in a project management and collaboration system. In an example embodiment, an apparatus detects an insight interface component request, accesses past development unit performance metrics data, determines a suggested development unit performance target, determines a selected development unit commitment, determines a visual emphasis element for the selected development unit commitment, wherein the visual emphasis element is configured to visually compare the selected development unit commitment to the suggested development unit performance target, generates a development unit performance summary insight interface component comprising the visual emphasis element, and outputs the development unit performance summary insight interface component for rendering to a project management user interface.

Techniques for managing a software build using a machine learning model are disclosed. A system obtains historical data associated with historical software builds. The historical data includes attribute data for a plurality of development stages associated with a historical software build and labels indicating success or failure for the plurality of development stages. The system trains a machine learning model using the historical data associated with the historical software builds to generate predictions of success or failure of the plurality of development stages. The system receives attributes of a target software build and a selection of a first target development stage of the target software build. The system applies the machine learning model to the target software build to generate a first prediction of success or failure of the first target development stage.

Techniques for managing a software build using a machine learning model are disclosed. A system obtains historical data associated with historical software builds. The historical data includes attribute data for a plurality of development stages associated with a historical software build and labels indicating success or failure for the plurality of development stages. The system trains a machine learning model using the historical data associated with the historical software builds to generate predictions of success or failure of the plurality of development stages. The system receives attributes of a target software build and a selection of a first target development stage of the target software build. The system applies the machine learning model to the target software build to generate a first prediction of success or failure of the first target development stage.

Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Provided are techniques for unified cognition for a virtual personal cognitive assistant. A personal cognitive agent creates an association with an entity and a personalized embodied cognition manager that includes an entity agent registry, wherein the personal cognitive agent comprises a virtual personal cognitive assistant. Selection of a first cognitive assistant agent from a first domain and a second cognitive assistant agent from a second domain are received. Input from the entity is received. A goal based on the input is identified. Unified cognition is provided by coordinating the first cognitive assistant agent of the first domain and the second cognitive assistant agent of the second domain to generate one or more actions to meet the goal. A response is provided to the input with an indication of the goal.