Hot reloading a running application with an unsaved source code change. A code change to a source code of a running software application that is associated with a project type is identified. The code change is stored within an in-memory editor buffer, and is uncommitted to any source code file. It is determined that the code change can be applied to the running software application using hot reload. Hot reload agent(s) associated with the project type are identified. Using the hot reload agent(s), the code change is communicated to an application runtime associated with the running software application. At least one process of the running software application invokes a new compiled code entity corresponding to the code change.

A computer readable storage medium, system and method for improving automated testing systems to include a first and second behavioral data. The first behavioral data is collected periodically and the second behavioral data is collected in real time. The receipt of the first behavioral data and a second behavioral data are followed by the receipt of a system configuration template. A test case is updated based on the first and second behavioral data, and an automated test environment is reconfigured based on the first behavioral data, second behavioral data, and the system configuration template. The test executes in the automated test environment producing a test result.

Systems and methods for code generation are described. The method may include accessing an application programming interface (API) specification written in a first computer programming language. The method may also include processing the API specification in the first computer programming language with a code generation tool. Furthermore, the method may include generating, from the API specification, code in each of a plurality of different computer programming languages different from the first computer programming language and different from each other.

A method includes receiving uncompilable code from a candidate. The method further includes extracting features from the uncompilable code. The method further includes outputting, with a coding machine-learning model, compilable code based on the uncompilable code and the extracted features. The method further includes generating a coding score based on the uncompilable code and the compilable code. The method further includes receiving first media of one or more answers to questions provided by the candidate during an interview. The method further includes outputting, with a media machine-learning model, one or more corresponding ratings for the one or more answers. The method further includes generating a media score based on the one or more corresponding ratings. The method further includes generating a total score based on the coding score and the media score.

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.

Disclosed herein are system, method, and computer program product embodiments for providing an auto-mapping recommendation between a source asset and a target asset in an integration flow design tool. Because the number of fields passed from a source asset to a target asset may be multitudinous, by auto-recommending mappings between fields provided by the source asset to the target asset, an integration flow design tool may save time developers a significant amount of time and optimize the integration flow design process.

According to some embodiments, systems and methods are provided, comprising receiving a code fragment exhibiting a static code issue; determining, via a trained exemption neural network, whether the received code fragment is exempt or not exempt from resolution; in a case it is not exempt, inputting the code fragment to a trained classification neural network; determining whether the static code issue is a syntactical static code issue or a non-syntactical static code issue; in a case it is a syntactical static code issue, inputting the code fragment to a first trained network to generate a first resolution; and in a case the static code issue is a non-syntactical static code issue, inputting the code fragment to a second trained network to generate a second resolution of the non-syntactical static code issue. Numerous other aspects are provided.

A driver configuration management method may be applied to a management device and includes determining target information, where the target information is used to represent a computing capability of an electronic device; converting, based on the target information, a configuration source file into a target configuration file that uses a target file format; and sending the target configuration file to the electronic device. The method may be applied to a scenario in which the management device generates and sends configuration files in different formats for electronic devices having different computing capabilities.

Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes storing on a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one stored MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the received sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Apparatuses, systems, and techniques to combine operations. In at least one embodiment, a processor causes two or more operations in a graph to be combined based, at least in part, on another combination of two or more independent operations.