The syntax elements of a source code program used to represent the context of a focal method are selected based on a priority order. The selected syntax elements are input into a fixed-size context window that is used to train a neural transformer with attention model to learn to generate source code and used by the neural transformer model to generate source code. The context window contains prioritized sequences of tokens that extend beyond the target focus in order to provide a longer visibility back into the source code program for the model to learn predictive patterns. This gives the model a file-level context of the source code program without increasing the size of the context window.

The syntax elements of a source code program used to represent the context of a focal method are selected based on a priority order. The selected syntax elements are input into a fixed-size context window that is used to train a neural transformer with attention model to learn to generate source code and used by the neural transformer model to generate source code. The context window contains prioritized sequences of tokens that extend beyond the target focus in order to provide a longer visibility back into the source code program for the model to learn predictive patterns. This gives the model a file-level context of the source code program without increasing the size of the context window.

A method for collaboration using a cell-based computational notebook is described. The method includes receiving a cell on a first computer from the cell-based computational notebook, the cell including executable code, the executable code including variables. The method further includes executing the executable code in the cell to generate a result and saving in a storage medium a state of the cell, the state of the cell including values of the variables associated with the executable code in the cell and the result. A system implementing the method is also disclosed.

A method of analyzing graphical user interface (GUI) objects. The method can include dynamically scanning attributes assigned to various GUI objects assigned to a view of a GUI in order to identify attributes associated with each of the GUI objects. For each of the GUI objects, a list of attributes can be generated. A determination can be made as to whether at least one of the GUI objects has a list of attributes that does not correspond to lists of attributes for other GUI objects. When at least one GUI object has a list of attributes that does not correspond to lists of attributes for other GUI objects, an identifier can be output. The identifier can indicate that the GUI object has the list of attributes that does not correspond to the lists of attributes for the other GUI objects.

A method of analyzing graphical user interface (GUI) objects. The method can include dynamically scanning attributes assigned to various GUI objects assigned to a view of a GUI in order to identify attributes associated with each of the GUI objects. For each of the GUI objects, a list of attributes can be generated. A determination can be made as to whether at least one of the GUI objects has a list of attributes that does not correspond to lists of attributes for other GUI objects. When at least one GUI object has a list of attributes that does not correspond to lists of attributes for other GUI objects, an identifier can be output. The identifier can indicate that the GUI object has the list of attributes that does not correspond to the lists of attributes for the other GUI objects.

Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Methods and systems for accomplishing a mission using a plurality of unmanned vehicles can include graphically describing the mission tasks at a graphical user interface (GUI) using Business Process Model Notation (BPMN), and translating the graphical description into extensible machine language (XML) formatted robot operating system (ROS) instructions, which can be understood by each of the plurality of unmanned vehicles with a translator. An execution engine transmits the XML ROS instructions to a respective local controller on the respective unmanned vehicle. The BPMN graphical descriptor symbols can allow for planning of a mission by an end user that does not have expertise in the ROS domain, and that does not have an understanding of the ROS construct. The execution engine can provide feedback back to the GUI regarding mission execution. Based on the feedback, the graphical description can be modified while the mission is being accomplished.

Systems and methods of generating voice-based software applications are provided. A system can receive, from an application developer computing device, a request to build a voice-based software application. The system can select an application template from a plurality of application templates. The selected application template can include a module that corresponds to a function of the voice-based software application. The system can provide the selected application template to the application developer computing device. The system can receive, from the application developer computing device, an input for a field of the at least one module of the selected application template. The system can generate the voice-based software application based on the selected application template and the input for the at least one field of the at least one module of the selected application template.

Systems and methods of generating voice-based software applications are provided. A system can receive, from an application developer computing device, a request to build a voice-based software application. The system can select an application template from a plurality of application templates. The selected application template can include a module that corresponds to a function of the voice-based software application. The system can provide the selected application template to the application developer computing device. The system can receive, from the application developer computing device, an input for a field of the at least one module of the selected application template. The system can generate the voice-based software application based on the selected application template and the input for the at least one field of the at least one module of the selected application template.