Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Disclosed herein are embodiments of systems, methods, and products for modernizing and optimizing legacy software. A computing device may perform an automated runtime performance profiling process. The performance profiler may automatically profile the legacy software at runtime, monitor the memory usage and module activities of the legacy software, and pinpoint/identify a subset of inefficient functions in the legacy software that scale poorly or otherwise inefficient. The computing device may further perform a source code analysis and refactoring process. The computing device may parse the source code of the subset of inefficient functions and identify code violations within the source code. The computing device may provide one or more refactoring options to optimize the source code. Each refactoring option may comprise a change to the source code configured to correct the code violations. The computing device may refactor the source code based on a selected refactoring option.

A computing system or application that enables generation and use of code block elements. A code block element refers to a graphic element that is rendered on a canvas to include content in the form of program code. In examples, the text content of the code block element can include syntax and other formatting that is in accordance with a selected programming language

In a cross-platform code conversion method, a conversion device obtains first source code that is configured to run on a first platform. The conversion device performs syntactic analysis on the first source code to generate a syntax tree corresponding to the first source code, and identifies a to-be-converted syntax block in the syntax tree according to a syntax rule provided by a rule library. The conversion device converts the to-be-converted syntax block according to a conversion rule provided by the rule library to obtain a converted syntax block. The conversion device then generates, based on the obtained converted syntax block, second source code for running on a second platform.

A processor circuit includes a compiler configured to receive a software program that comprises software code coded in an assembly language and inline software code coded in a high-level programming language, compile the inline software code coded in the high-level programming language within the software program into assembly code in the assembly language, and compile the assembly code and the software code coded in the assembly language into machine code for the processor circuit. A method includes determining if first and second instructions in a software program are combinable into one instruction word, combining the first and the second instructions in the software program into one instruction word if the first and the second instructions are combinable, and fetching the instruction word into a single register by storing the instruction word in the single register.

Systems and methods provide a generic factory class to determine one or more classes implementing an interface and/or derived from a base class in response to a call from an application factory class by retrieving a list of the one or more classes implementing the interface or derived from the based class, determining properties of each of the one or more classes, and return, based on the properties, a name of each of one or more of the one or more classes.

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.

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 receiving at 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 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 sample and without performing dynamic analysis of the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

According to an aspect of an embodiment, operations for code enrichment through metadata for code synthesis are provided. The operations include acquiring package data that include source code files and package metadata. The operations further include extracting additional metadata associated with software package and preparing metadata features based on the package metadata and the additional metadata. The operations further include identifying a set of target portions of a source code included in the source code files and updating one or more source code files using the metadata features. Such files are updated by performing at least one of a revision of existing code comments, and an addition of new code comments for the target portions. The operations further include generating a dataset of natural language (NL) text features and respective code features and training a language model on a sequence-to-sequence generation task.

A document to be stored on a network-based storage system is identified. The document includes one or more macros in a first programming language. An object referenced by a function defined by a macro of the one or more macros is identified. The function is converted into one or more sets of operations represented in a second programming language. Each set of operations corresponds to one of one or more candidate object types associated with the object. At least one of the one or more sets of operations is to be performed with respect to the object responsive to indication of a corresponding candidate object type for the object during execution of the macro. The document including the one or more sets of operations represented in the second programming language is stored on the network-based storage system.