Abstract: Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.

Abstract: In some implementations, a code translation validation platform may convert source code, associated with a source application, to an intermediate representation that supports a source programing language and a target programing language. The code translation validation platform may identify metamorphic relations in the intermediate representation based on program constructs and program interactions. The code translation validation platform may generate test cases based on the identified metamorphic relations. The code translation validation platform may perform symbolic execution of the intermediate representation to generated test cases. The code translation validation platform may apply the generated test cases to the source programming language and to the target programming language.

Abstract: A domain transformation system may determine a source ontology for a source domain of a first environment. The source domain relates to a process performed in the first environment. The process may be transformed from the source domain to a target domain of a second environment, such as a virtual environment. The domain transformation system may determine a first portion of a target ontology for the target domain. The domain transformation system may generate, using a machine learning technique, a first embedding of the source ontology and a second embedding of the target ontology. The domain transformation system may generate a joint embedding based on the first embedding and the second embedding domain transformation system and may determine a second portion of the target ontology, based on the joint embedding, using a transfer learning technique. The domain transformation system may refine the target ontology using a neuro-symbolic artificial intelligence technique.