6212results about "Software reuse" patented technology

A customized prompt generation service automates prompts to a large language model to perform a specified software engineering task. The service stores the custom data of a client that includes code diff hunks, source code segments, code reviews, repaired code, and unit tests from a code base or repository of the client. Prompt templates are associated with each software engineering task that include the requisite information needed for the large language model to perform the target task. A prompt to a large language model includes examples of the software engineering task from the custom data of the client of the service.

An example operation may include one or more of receiving a first code base and a second code base from a software repository, wherein each of the first and second code bases comprise source code of a plurality of different software programs, identifying a source code within the first code base that is interdependent with a source code within the second code base based on execution of a generative artificial intelligence (GenAI) model on the first and second code bases, determining a software component to connect the source code within the first code base to the source code within the second code base, and displaying an identifier of the software component via a user interface.

Systems and methods for building an application incorporating AI functionality include enabling access to a developer application framework associated with a SaaS platform, receiving application code for generating an application, and receiving a selection of an AI assistant add-on provided by the SaaS platform. A link is established between the selected AI assistant add-on and a SaaS platform element, permission is provided to access data from the at least one of the plurality of linked SaaS platform elements. Based on the permissions, data is transferred from the at least one of the plurality of linked SaaS platform elements, and AI integration code is received including at least one reference to the transferred data and at least one call to an AI agent. The application is published for selective use with SaaS platform elements and is configured to exchange data with the at least one of the plurality of SaaS platform elements.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on execution of the GenAI model on software libraries and descriptions of intent of the software libraries, receiving a first set of software libraries and a second set of software libraries, identifying a first software library within the first set of software libraries that includes redundant functionality with a second software library within the second set of software libraries based on execution of a generative artificial intelligence (GenAI) model on the first and second sets of libraries, and displaying an identifier of the first and second software libraries via a user interface.

A generative artificial intelligence (AI) driven code fixing pipeline has been created that uses a transformer-based large language model (LLM) to patch flawed program code. A pre-trained LLM is fine-tuned to generate a response that is a modified version of a code fragment in a prompt to the pre-trained model. After fine-tuning, the pre-trained LLM (hereinafter “code fix model”) is integrated into a pipeline that includes a program code cybersecurity scanner and a prompt generator. The scanner generates indications of flaws in program code and weakness types for those flaws. These indications flow into the prompt generator. The prompt generator retrieves reference code pairs based on weakness type and programming language to generate a batch of prompts to run inference on with the code fix model. The responses generated by the code fix model are presented as patching alternatives.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, generating one or more prompts and displaying the one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a design of a structure based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the design of the structure via the user interface of the user profile page of the software application.

Systems and methods for software development using machine learning models are disclosed. In one embodiment, a method for programmatically generating source code using a machine learning model includes obtaining a set of project configuration information that defines a programming task, indexing original source code in a code repository, advancing the programming task by constructing a prompt to a machine learning model to obtain new source code as an output, writing the new source code to the code repository, identifying an intervention request that requires human input to complete the programming task, obtaining human input for the intervention request from a human using digital communications as an outcome, and writing the intervention request and outcome to an intervention database.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses a low-code platform based on a component library and a form engine, and relates to the field of data processing. The platform comprises a design system, an application system and a service system. The design system is provided with a form designer and a template manager, the form designer can configure a form component to generate a description file, and the template manager supports separate storage of a page template; the application system comprises a form analysis engine, a UI rendering engine and a reusable resource management component, the analysis engine analyzes configuration generation logic, the rendering engine loads a resource rendering page, and the reusable resource management component stores resources and generates configuration; the service system comprises a metadata analysis engine and an SQL assembler, the metadata analysis engine analyzes business rules to generate SQL and filtering strategies, and the SQL assembler generates operation statements adaptive to multiple data sources. Through the modular architecture design and the dynamic analysis mechanism, the problem that a low-code platform is insufficient in flexibility and usability is solved.

The invention discloses a method and system for dynamically calling a Java statistical analysis interface based on LLM and MCP protocols, belongs to the technical field of data processing and analysis technology, artificial intelligence technology and application program interface (API) integration, and aims to solve the technical problem of how to improve the response speed and flexibility of statistical analysis requirements. In order to reduce the development and maintenance cost of a statistical analysis function, the adopted technical scheme is as follows: a Java annotation mechanism is utilized to mark and describe an existing Java statistical analysis system, a Java method of which the annotation is defined is obtained, and the Java method of which the annotation is defined can be automatically identified by an MCP server and registered as an MCP tool; the MCP server follows an MCP protocol and communicates with the LLM serving as an MCP client, and the LLM dynamically discovers available MCP tools through the MCP protocol; after natural language query of a user is understood through LLM, one or more appropriate MCP tools are intelligently selected, calling parameters are generated, and an MCP server is requested to execute through an MCP protocol.

A method for creating and deploying plugins for use with one or more web-hosted large language model applications includes: displaying a user interface comprising a code template for creation of a plugin, wherein the code template comprises a context template, a native function template, and a semantic prompt template; receiving modifications to the code template via the user interface to form a modified code template, the modified code template comprising: custom context based on the context template; custom native code comprising one or more computer executable functions based on the native function template; and a custom prompt configured to prompt a large language model based on the semantic prompt template; generating a plugin based on the modified code template; and storing the plugin within a database of a plugin registry.

A system allows generation of information used by an integrated development environment using a machine learning-based language model, for example, a large language model. The integrated development environment is for developing applications, e.g., database applications. The system receives a natural language request for information related to development of the database application. The system determines contextual information describing a development task associated with the database application and generates a prompt for a machine learning based language model based on the natural language request and the contextual information and receives a response. The system extracts the information related to development of the database application from the response. In response to a natural request, the system may generate a database query, provide a resultset by executing the database query, automatically determine a type of chart and generate one or more charts for visually displaying the result.

Systems, methods, and other embodiments automated fine-tuning of chatbot performance for large language models are described herein. In one embodiment, a method accesses a collection of sample conversations between two entities. An individual sample conversation includes one or more rounds of natural language example prompt by a querent and example response by an agent. The method fine-tunes an LLM to generate responses in natural language based on a chatbot loss function that evaluates first responses generated by the LLM to the example prompts by the querent. The method generates an evaluation score for performance of the tuned LLM as a chatbot based on second responses generated by the tuned LLM to test prompts from a test conversation. And, the method automatically signals that the fine-tuning of the tuned LLM is complete in response to the evaluation score satisfying a threshold.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

In some embodiments, generative systems are physical mechanics and hardware apparatus to generate display content and include an input receiver module, a graphics processor module, and an output module. In some embodiments, the input receive module converts or splits a video input signal to provide a video input stream for the graphical processing module, which includes one or more processing units. The graphics processor module extracts both the graphical input content and the metadata of the input stream, operates on said information, and outputs the result to the output module, which emits an output signal for display on a display system. In some embodiments, the graphics processor module uses a generative function or a computer vision function. In some embodiments generative system is a standalone device to mediate and infer visual content between a computational system and a display system.

An artificial intelligence (AI) / machine learning (ML) recommendation engine for robotic process automation (RPA) is disclosed. An AI / ML model may be trained to provide recommendations for a next activity, a next sequence of activities, and / or modifications to parameters for one or more existing activities to include during RPA workflow development. The recommendations may be based on the context of where the user is in the RPA workflow. For user interface (UI) automations, the AI / ML model may be linked to an object repository and trained to make recommendations therefrom. The AI / ML model may also be trained to generate new UI descriptors for the object repository.

Systems, methods, and other embodiments associated with automated fine-tuning of software code generation by large language models are described herein. In one embodiment, a method accesses a collection of software code samples that intermix sample code and human language description. The method generates prompts to an LLM to write code that performs as described by the human language description of the sample code. The method fine-tunes a large language model to generate software code based on a code generation loss function that evaluates code generated by the LLM in response to the prompts. The method generates an evaluation score for performance of the tuned large language model as a code generator based on code generation loss for second generated code. And, the method automatically signals that fine-tuning of the tuned large language is complete in response to the evaluation score satisfying a threshold.

The invention provides an enterprise production data micro-service granularity optimization method, a medium and a system, belongs to the technical field of production data management, and establishes a micro-service granularity evaluation equation set by obtaining a dependency relationship and business flow data of micro-services in an enterprise production data system, constructing a coupling degree and cohesion degree matrix and establishing a micro-service granularity evaluation equation set. According to the method, a hybrid optimization algorithm is adopted, the search direction of a grey wolf algorithm and group evolution of a genetic algorithm are combined, and a composite fitness function containing system response time, resource utilization rate, maintenance cost and micro-service granularity balance degree is constructed. And continuously updating the micro-service division scheme through iterative optimization until the preset number of iterations is reached and the composite fitness function meets the optimization target, and finally outputting the optimal micro-service division scheme. The technical problem that the micro-service granularity is difficult to determine in the prior art is solved according to the division scheme of multiple indexes such as the maintenance cost and the granularity balance degree.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

A code insertion engine predicts one or more statements of a programming language to be inserted at an insertion point in between existing source code statements of a source code program being edited. The code insertion engine extracts the surrounding context of the insertion point which includes the source code immediately preceding and the source code immediately following the insertion point. The code insertion engine uses a neural expansion model and a neural selector model to predict the one or more statements most likely to be inserted into the insertion point that are syntactically and semantically consistent with the surrounding context of the existing program.

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Techniques are described for deploying software applications associated with document management. The techniques may include retrieving, by a computing system and from a software repository, configuration information for a software application for managing an electronic document, the configuration information specifying a plurality of widgets for deploying the software application. The techniques may further include receiving, by the computing system and from a client device, a request to access the software application. The techniques may further include generating, by the computing system, a response to the request comprising an indication of the configuration information and instructions configured to cause the client device to request, from the software repository, the plurality of widgets specified by the configuration information. The techniques may further include outputting, by the computing system, the response to the client device to provide the software application at the client device.

A system and method including receiving a prompt specifying at least one task type; determining a system prompt based on the received prompt, the system prompt including artificial intelligence (AI) system configuration details corresponding to the at least one task type; pre-processing the system prompt to generate a pre-processed prompt including code referenced in the system prompt; transmitting, as an input prompt, the pre-processed prompt to an AI system; receiving, in response to the AI system executing the pre-processed prompt, a result from the AI system; and storing a record of the result from the AI system in a data repository.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a page generation method and device based on a natural language, equipment and a medium, and the method comprises the steps: obtaining a demand description in a natural language form, analyzing the demand description based on a preset intention recognition material, and generating a user intention; retrieving in the page feature vector library according to user intention to obtain a page template, performing graphical user interface rendering on the page template to generate a visual component set, injecting associated data into a target component in the visual component set to generate a target page, publishing the target page or publishing and collecting use effect data after modification, and displaying the use effect data. And updating the page feature vector library by using the use effect data. By combining natural language analysis, intention recognition, vector retrieval, graphical rendering and effect data updating, automation from demand input to page generation is realized, manual intervention and development cycle are greatly reduced, and page generation efficiency and quality are improved.

An application mapping engine may determine dependencies in existing executable application by monitoring and analyzing process flow and data flow of the application during runtime execution. The application mapping engine may generate dependency visualization maps that illustrate dependency interaction information.

The invention discloses a multi-person collaborative programming method and system, electronic equipment and a medium, and the method comprises the steps: providing a canvas task designer which supports the visual design of a plurality of canvases, and enabling each canvas to correspond to a subtask; sub-tasks designed in one or more canvases are stored as independent module files, and each module file can be developed by different developers; persistently storing the developed module file locally, and creating a module ID for the module file; and importing each module file into a unified project, and editing a data reference relationship and execution logic among the module files. According to the multi-person collaborative programming method and system, the electronic equipment and the medium provided by the invention, the whole project is divided into a plurality of independent sub-tasks through the canvas task designer, and each sub-task is stored in the form of the module file and can be independently developed by different developers.

The invention provides a rapid construction and reuse method and system for an application framework, and relates to the technical field of application framework construction, and the method comprises the following steps: mapping code snippets to a high-dimensional tensor space, extracting multi-level code features, and constructing a code association graph and a deep semantic network; constructing a hierarchical Bayesian network based on the spatial-temporal feature map, executing variational reasoning and adaptive particle filtering, and generating a reconstruction code; driving a multi-level generation network by using a dual-dimension discriminator, optimizing a code structure and a dependency relationship, and generating a final optimization code through program slicing and incremental synthesis; according to the method, rapid construction and reuse of the application framework are realized, and the code quality and the execution efficiency are improved.

A computer-implemented method can specify a source version and a target version of an application programming interface (API) and a target programming language; and retrieve a difference graph connecting from a source knowledge graph characterizing the source version of the API to a target knowledge graph characterizing the target version of the API. The difference graph includes one or more revision edges representing changes of the API between the source version and the target version. The method can install one or more function packages written in the target programming language and associated with the one or more revision edges; and run the one or more function packages to update the API from the source version to the target version.