A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

This application relates to a computing system and method for an automated program error repair. In one aspect, the computing system includes a storage, a preprocessing processor, and an automated error repair processor. The storage stores a program code. The preprocessing processor acquires the program code from the storage and preprocesses the program code. Preprocessing includes tokenizing the program code with tokens, converting the tokens into vectors, and adding location information for the tokens. The automated error repair processor receives the preprocessed program code as an input from the preprocessing processor, detects an error in the preprocessed program code, corrects the detected error, and outputs the error-corrected program code. Detecting and correcting the error are performed based on a deep learning result and the location information for the tokens.

Systems, computer program products, and methods are described herein for software compiler integrity verification. The present invention is configured to retrieve, from a source code repository, a source code; process, using a first build machine, the source code into a first object code; process, using a second build machine, the source code into a second object code; initiate an integrity verification engine on the first object code and the second object code; decompile, using the integrity verification engine, the first object code to create a first decompiled object code and the second object code to create a second decompiled object code; compare the first decompiled object code with the second decompiled object code; determine a match between the first decompiled object code and the second decompiled object code; and transmit an approval notification.

Techniques are disclosed for utilizing configurable delays in an instruction stream. A set of instructions to be executed on a set of engines are generated. The set of engines are distributed between a set of hardware elements. A set of configurable delays are inserted into the set of instructions. Each of the set of configurable delays includes an adjustable delay amount that delays an execution of the set of instructions on the set of engines. The adjustable delay amount is adjustable by a runtime application that facilitates the execution of the set of instructions on the set of engines. The runtime application is configured to determine a runtime condition associated with the execution of the set of instructions on the set of engines and to adjust the set of configurable delays based on the runtime condition.

An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

Apparatuses, systems, and techniques to add operators to a compiler. In at least one embodiment, one or more operators are added to a compiler using one or more application programming interfaces (APIs).

An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

A computer-implemented low-code development platform is provided including a user interface and having access to a library of step macros configured for user configuration and interconnection via the user interface to generate executable code. Each step macro includes a step configuration generator and an execution code generator. The step configuration generator is configured to generate a step configuration file based on user-configurable data points configurable via the user interface. The execution code generator is configured to generate executable code in the form of a compiled step file configured for storage in memory and execution by a processor of a computing system. The execution code generator receives and inputs the step configuration file into a metaprogramming component configured to interpret the user-configurable data points of the step configuration file and to generate and output the compiled step file.

Embodiments described herein provide techniques for in-line compiling of UDFs in other programming languages. These techniques enable faster programming iterations because it allows users to compile directly in the cloud processing system. Moreover, it allows the UDFs to tie into existing libraries. The compiled results are treated as sensitive and handled with appropriate security policies, as with any other user data in the system.

Disclosed is a system for converting a high-level runtime model to a low-level runtime model where the high-level runtime model runs on a client computer system, and the low-level runtime model runs on a server computer system. The server system has installed thereon a pool of hardware accelerators, and the low-level runtime model is targeted to the pool of accelerators. Outputs of the low-level runtime model are returned to the high-level runtime model as if the high-level runtime model computed the outputs.