A machine learning model is trained to translate source code from one or more programming languages into a common programming language. The machine learning model translates source code from the other languages into the common programming language. A language embedder generates a vector for each function in the source code, all of which is now in the common programming language. A user provides a text search query which is converted by a language embedder to a vector. Based on the vector of the text search query and the vectors for the source code, search results are generated and presented in a user interface. Additional machine learning models may be trained and used to measure function complexity, test coverage, documentation quantity and complexity, or any suitable combination thereof. These measures may be used to determine which search results to present, an order in which to present search results, or both.

A machine learning model is trained to translate source code from one or more programming languages into a common programming language. The machine learning model translates source code from the other languages into the common programming language. A language embedder generates a vector for each function in the source code, all of which is now in the common programming language. A user provides a text search query which is converted by a language embedder to a vector. Based on the vector of the text search query and the vectors for the source code, search results are generated and presented in a user interface. Additional machine learning models may be trained and used to measure function complexity, test coverage, documentation quantity and complexity, or any suitable combination thereof. These measures may be used to determine which search results to present, an order in which to present search results, or both.

A mixed mode programming method permitting users to program with graphical coding blocks and textual code within the same programming tool. The mixed mode preserves the advantages of graphical block programming while introducing textual coding as needed for instructional reasons and/or for functional reasons. Converting a graphical code block or group of blocks to a textual block lets the user see a portion of the textual code in the context of a larger program. Within one programming tool the mixed mode method allows users to learn programming and build purely graphical blocks; then transition into mixed graphical and textual code and ultimately lead to their ability to program in purely textual code. The mixed mode further allows users to program using any combination of drag-and-drop graphical blocks and typed textual code in various forms.

A mixed mode programming method permitting users to program with graphical coding blocks and textual code within the same programming tool. The mixed mode preserves the advantages of graphical block programming while introducing textual coding as needed for instructional reasons and/or for functional reasons. Converting a graphical code block or group of blocks to a textual block lets the user see a portion of the textual code in the context of a larger program. Within one programming tool the mixed mode method allows users to learn programming and build purely graphical blocks; then transition into mixed graphical and textual code and ultimately lead to their ability to program in purely textual code. The mixed mode further allows users to program using any combination of drag-and-drop graphical blocks and typed textual code in various forms.

A system and a method are disclosed containerizing a source code file. In some embodiments, the system detects a command to navigate a user interface to a machine station. The system responsively generates for display using the user interface a station identifier corresponding to the machine station and a drag-and-drop interface. The system receives a source code file by way of a drag-and-drop operation being performed with respect to the drag-and-drop interface. The system selects a machine of the machine station to execute the source code file, containerizes the source code file based on a language used by the selected machine, and commands the selected machine to execute the containerized source code file. The system generates for display results of the executed containerized source code file using the user interface.

Systems, methods, and storage media for creating an interface between a smart contract to be executed on a decentralized architecture and a user component, the method comprising: receiving code corresponding to the smart contract at an interface server; the interface server parsing an application binary interface (ABI) corresponding to the smart contract; the interface server constructing an enhanced application binary interface (EABI) based on the ABI; and the interface server creating a REST API interface specific to the smart contract based on the EABI.

Systems, methods, and storage media for creating an interface between a smart contract to be executed on a decentralized architecture and a user component, the method comprising: receiving code corresponding to the smart contract at an interface server; the interface server parsing an application binary interface (ABI) corresponding to the smart contract; the interface server constructing an enhanced application binary interface (EABI) based on the ABI; and the interface server creating a REST API interface specific to the smart contract based on the EABI.

Code translation is an evolving field and due to advancements in the infrastructure and compute power. The existing methods for code translation are time and effort intensive. A method and system for translation of codes based on the semantic similarity have been provided. A machine learning model is developed, that understands and encapsulates the semantics of the code in the source side and translates the semantic equivalent code which is more maintainable and efficient compared to one to one translation. The system is configured to group a plurality of statements present in the source code together into blocks of code and comprehend the semantics of the block. The system is also trained to understand syntactically different but semantically similar statements. While understanding the semantics of the block and translating, the unused/duplicate code etc. gets eliminated. The translated code is better architected and native to the target environment.

Code translation is an evolving field and due to advancements in the infrastructure and compute power. The existing methods for code translation are time and effort intensive. A method and system for translation of codes based on the semantic similarity have been provided. A machine learning model is developed, that understands and encapsulates the semantics of the code in the source side and translates the semantic equivalent code which is more maintainable and efficient compared to one to one translation. The system is configured to group a plurality of statements present in the source code together into blocks of code and comprehend the semantics of the block. The system is also trained to understand syntactically different but semantically similar statements. While understanding the semantics of the block and translating, the unused/duplicate code etc. gets eliminated. The translated code is better architected and native to the target environment.

A software deployment method includes: analyzing a source code of an application; designating off-loadable processes of the application; performing a code conversion of the application according to a deployment destination environment; measuring the performance of the converted application on a verification device; making a setting for resource amounts according to the deployment destination environment; selecting a deployment place by calculating a deployment destination on the basis of a performance and a cost when the converted application is deployed while ensuring the resource amounts; performing, after deployment to an actual environment, a performance measurement test process to measure an actual performance of application; and performing, after performing the performance measurement test process, one or more of performing the code conversion, making the setting for resource amounts, selecting the deployment place, measuring the performance of the application on the verification device, and performing the performance measurement test process.