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 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 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 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 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 label and a method for providing a label to a user is described. The label may be a mailing label to be placed on letters or parcels. The label includes information such as postage, delivery address, return address, and a barcode for tracking delivery information. A user may request both a label and postage in what, to the user, appears to be a single computerized connection on a single internet web-page. A postage indicia is provided through a postage provider with whom the user has a deposit or credit. The label itself may be generated through a label-generating application hosted or generated by an entity separate from the postage provider.

Abstract: A label and a method for providing a label to a user is described. The label may be a mailing label to be placed on letters or parcels. The label includes information such as postage, delivery address, return address, and a barcode for tracking delivery information. A user may request both a label and postage in what, to the user, appears to be a single computerized connection on a single internet web-page. A postage indicia is provided through a postage provider with whom the user has a deposit or credit. The label itself may be generated through a label-generating application hosted or generated by an entity separate from the postage provider.

Abstract: A durable, clear, protective cover for a device having a touch screen, keypad, or other user interface. The cover protects the device without interfering with the ability to use the device. The protective cover is constructed by vacuum-forming a thin sheet of transparent polyurethane or polyvinyl chloride (PVC) on the front and at least a portion of the sides of a mold made of the target device. Accordingly, the protective cover made from a single piece of material will have the topography matching the device, which facilitates in the continued usability of the device when the protective cover is disposed on the device. Furthermore, the protective cover fits securely on the device such that no adhesives or other mechanisms are required to secure the protective cover onto the device, nor are any tools required to install or remove the protective cover.

Abstract: A label and a method for providing a label to a user is described. The label may be a mailing label to be placed on letters or parcels. The label includes information such as postage, delivery address, return address, and a barcode for tracking delivery information. A user may request both a label and postage in what, to the user, appears to be a single computerized connection on a single internet web-page. A postage indicia is provided through a postage provider with whom the user has a deposit or credit. The label itself may be generated through a label-generating application hosted or generated by an entity separate from the postage provider.