Abstract: A system stores a source code change, at a location in source code associated with a software engineering task, received from a software developer's code editor. The system receives a request from the code editor for predicted source code changes at a source code location, and retrieves context data which establishes the software engineering task's context. The system transforms the context data to be compatible with the data format used to train a machine-learning model to assist with performing software engineering tasks. The machine-learning model uses the transformed context data to predict source code changes at the source code location. The system outputs the predicted source code changes at the source code location to the software developer's code editor. The system commits source code changes based on any predicted source code changes at any source code locations, as accepted by the code editor.

Abstract: A system receives a request from a software developer's issue tracker or code editor to perform a software engineering task, and outputs an issue report which describes the software engineering task and/or source code for performing the software engineering task to the software developer. The system stores the software developer's update of the issue report and/or source code changes for the software engineering task. The system receives the software developer's request for a predicted completion of the software engineering task, retrieves the software engineering task's context data, and transforms the context data to be data format compatible with a machine-learning model that learned to assist with software engineering tasks. The machine-learning model uses the transformed context data to predict completions of the software engineering task.

Abstract: A system stores a source code file's changes from a software developer's code editor, for a software engineering task. Upon receiving the code editor's request to predict source code for the source code file, the system retrieves the software engineering task's context data, and transforms the context data to be compatible with the data format used to train a machine-learning model to assist with performing software engineering tasks. The machine-learning model uses the transformed context data to predict the source code for the source code file, with source code file portions corresponding to predicted source code portions. The system identifies each portion of the source code file which is differing from a corresponding portion of the predicted source code, via the code editor. The system commits any differing portions of the predicted source code, which are requested and accepted by the code editor, to the source code file.

Abstract: A system that trains a machine-learning model to assist with performing software engineering tasks is described. The system retrieves data from data sources associated with software engineering tasks. The system links the data by linking each issue report which describes any one of the software engineering tasks with source code associated with the any one of the software engineering tasks. The system transforms the data to be compatible with a data format used to train a machine-learning model to assist with performing software engineering tasks. The system trains the machine-learning model with the transformed data to assist with performing a software engineering task by making a prediction of source code changes associated with the software engineering task.

Abstract: A system receives a request, from an issue tracker of a software engineering task's stakeholder, to begin an incomplete issue report which describes the software engineering task for a software developer, and assigns the software engineering task to the software developer. The system receives the stakeholder's request to predict a completion of the incomplete issue report, retrieves the software engineering task's context data, and transforms the context data to be data format compatible with the machine-learning model that learned to assist with software engineering tasks. The machine-learning model uses the transformed context data to predict the completion of the incomplete issue report.

Abstract: A system receives a request from a software developer's issue tracker to review an issue report which describes a software engineering task, and outputs the issue report to the issue tracker. The system receives the issue tracker's request for predicted source code changes for the software engineering task, retrieves context data which establishes the software engineering task's context, and transforms the context data to be compatible with the data format used to train a machine-learning model to assist with performing software engineering tasks. The machine-learning model uses the transformed context data to predict source code changes for the software engineering task. The system output the predicted source code changes for the software engineering task to the issue tracker. The system commits source code changes based on the predicted source code changes, as accepted by the issue tracker, to source code associated with the software engineering task.

Abstract: A dynamically evolving cognitive architecture system based on prompting for additional user input is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: Dynamically evolving cognitive architecture system planning is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on a natural language intent interpreter is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on training by third-party developers is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on third-party developers is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on contributions from third-party developers is described. A system receives a span of natural language annotated with an object from a first third-party developer. The system forms an intent based on a user input, which includes a natural language span which corresponds to an action object, a first concept object, and/or a second concept object. The action object, the first concept object, and/or the second concept object is provided by a second third-party developer. The annotating object is the action object, the first concept object, or the second concept object. Forming the intent enables executing the action object to transform the first concept object into the second concept object based on the annotated span of natural language, and also enables outputting a value associated with the second concept object associated with a goal of the intent.

Abstract: A dynamically evolving cognitive architecture system based on contributions from third-party developers is described. A system receives a span of natural language annotated with an object from a first third-party developer. The system forms an intent based on a user input, which includes a natural language span which corresponds to an action object, a first concept object, and/or a second concept object. The action object, the first concept object, and/or the second concept object is provided by a second third-party developer. The annotating object is the action object, the first concept object, or the second concept object. Forming the intent enables executing the action object to transform the first concept object into the second concept object based on the annotated span of natural language, and also enables outputting a value associated with the second concept object associated with a goal of the intent.

Abstract: A dynamically evolving cognitive architecture system based on training by third-party developers is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: Dynamically evolving cognitive architecture system planning is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on a natural language intent interpreter is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on third-party developers is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A dynamically evolving cognitive architecture system based on prompting for additional user input is described. A system forms an intent based on a user input, and creates a plan based on the intent. The plan includes a first action object that transforms a first concept object associated with the intent into a second concept object and also includes a second action object that transforms the second concept object into a third concept object associated with a goal of the intent. The first action object and the second action object are selected from multiple action objects. The system executes the plan, and outputs a value associated with the third concept object.

Abstract: A modular uninterruptible power supply is disclosed, which provides complete redundancy for all components required for UPS operation. Novel aspects of the invention include design of the modules and their interconnection and interoperability, as well as improved operation techniques applicable to UPS systems generally.

Abstract: A modular uninterruptible power supply is disclosed, which provides complete redundancy for all components required for UPS operation. Novel aspects of the invention include design of the modules and their interconnection and interoperability, as well as improved operation techniques applicable to UPS systems generally.