4278 results about "Software development" patented technology

Some embodiments engineer a prompt for submission to a language model, such as a software development large language model. Some embodiments ascertain a relationship between code development information and potential context. Code development information includes static analysis results, project settings, development tool history or status data, and other software development data which augments training data previously embedded in the language model. Some embodiments compute a prompt inclusion score of the potential context, based on at least the relationship, and use the inclusion score to determine whether to include the potential context in the language model prompt. In some scenarios, an embodiment determines where to place the context in the prompt. Scoring is performed by a formula, statistical scoring model, or machine learning scoring model. Some embodiments reduce context inclusion false positives and false negatives that were based on the use of embedding similarity scores alone.

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 relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to a code generation method and system based on heterogeneous multi-agent collaboration and electronic equipment. According to the method, a special commentator agent instantiated by a heterogeneous large language model is added into a multi-agent collaboration process; according to the method, consistency and feasibility verification is carried out on a demand document given by an analyst agent by adopting a commentator agent, a modification suggestion is proposed in an early stage, deviation and problems are found in time in the early stage of a task, the cost and complexity of later correction are reduced, the robustness and reliability of codes are improved, and the method is suitable for large-scale popularization and application. And the generated result can still maintain high-level correctness under extreme conditions or specific boundary conditions. And the method has practical significance in improving software development efficiency, reducing project risks and improving the quality of an intelligent automatic solution.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention relates to the technical field of software development, and particularly provides a self-adaptive code annotation updating method and system, a terminal and a storage medium, and the method comprises the following steps: confirming that codes are updated, extracting an updated code set, and combining and constructing an abstract syntax tree for the codes; performing semantic analysis on the abstract syntax tree by using a deep learning model to generate a semantic vector, and constructing a program dependency graph based on the semantic vector; analyzing a dependency relationship among code elements in the program dependency graph by utilizing a graph neural network; and generating a code annotation based on the semantic vector and the dependency relationship between the code elements by using a natural language generation model. According to the method, the real-time performance and the accuracy of the code document are ensured, the readability and the maintainability of the code are greatly improved, and the maintenance cost is reduced.

A method for automating a software development lifecycle by using artificial intelligence is disclosed. The method includes receiving a request to generate software components, the request including requirements in a natural language format; determining, by using a first model, tasks to generate the requested software components based on the request, each of the tasks including a sequence of subtasks, an acceptance criterion, and a predicted effort level; generating, by using a second model, sets of software code based on the tasks, the sets of software code corresponding to the requested software components; generating, by using a third model, unit tests for each of the sets of software code; initiating the unit tests for each of the sets of software code; and releasing, by using a fourth model, the sets of software code based on a result of the corresponding unit tests.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

The invention relates to the technical field of software development management, in particular to an internet software development management system and method. The method comprises the following steps: collecting an original function demand set and carrying out function clustering analysis; extracting and classifying demand function features and generating independent demand units; carrying out dependency analysis on the independent demand units; constructing a dependency relationship set and a demand logic structure; the method comprises the following steps: generating a demand framework matrix, carrying out organization network simulation to obtain a demand connection network, carrying out code reverse evolution according to the demand connection network, carrying out basic parameter matching to obtain evolution basic parameters, carrying out a pressure simulation test on the evolution basic parameters to generate a pressure test result, and capturing resource pressure defects based on the pressure test result. And optimizing and adjusting the evolution basic parameters to generate optimized development parameters. According to the invention, a more efficient Internet software development management method is realized.

The invention discloses a warehouse level code problem solving system based on a large language model, and relates to the technical field of software engineering, a source code file is extracted from a code warehouse, a static analysis tool is utilized to analyze codes, a warehouse level code graph is constructed, and then code entities and dependency relationships thereof are analyzed; based on the warehouse-level code graph, combining historical problem solving data, and constructing a dynamic evolution knowledge graph through continuous learning; through a context retrieval module, according to the context information of the current problem, dynamically retrieving related solution strategies and generating corresponding code patches, complementing or reviewing suggestions; the validity of the generated patch is verified through static analysis, unit testing and integration testing, and a problem solving strategy is optimized according to feedback of a developer. The problem solving efficiency and accuracy can be improved, the development cost is reduced, the solving process is gradually optimized through an intelligent feedback mechanism, a closed-loop feedback mechanism is formed, and therefore the increasingly complex software development requirements are met.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects. In one aspect, a method includes obtaining data indicative of initial project plan parameters associated with a software development project; identifying multiple program modules associated with the software development project; obtaining, for a first set of modules of the multiple modules, corresponding source code segments; executing a quality assessment process to compute, for each corresponding source code segment, a corresponding quality score; identifying, based on the quality scores, that at least a subset of the corresponding source code segments are integrable into a corresponding first subset of the first set of modules; and generating a revised project plan by generating updates to the initial project plan parameters by accounting for the corresponding source code segments being integrated in to corresponding ones of the first set of modules.

The invention is applicable to the technical field of software development, and provides a code review optimization method, which comprises the following steps of: analyzing a demand description to generate a demand analysis result; preprocessing the software code to obtain a static analysis result; running the software code, and dynamically analyzing the running software code to obtain a dynamic analysis result; carrying out fusion analysis on the demand analysis result, the static analysis result and the dynamic analysis result by adopting a deep learning model, and identifying vulnerability points of the software code; generating a comprehensive vulnerability feature library based on historical vulnerability data and expert knowledge; and performing risk assessment and vulnerability evolution prediction on the vulnerability points to obtain an analysis risk score and security threat points within a preset time, and generating a code optimization strategy. According to the method, optimization can be carried out according to actual problems encountered by developers in the development process, it is ensured that code modification directly aims at the specific problems, the optimization precision and efficiency are improved, meanwhile, the prediction function is achieved, and therefore the code safety is improved.

There is disclosed herein a method of providing protected access to a third-party generative artificial intelligence (GAI) for software development including transforming a source code input (a transformed source code) via a one-way transform, wherein an enterprise has a proprietary interest in the source code input, and wherein the one-way transform is not internally reversible; providing the transformed source code to the third-party GAI, in association with a query for the third-party GAI, wherein the third-party GAI is controlled by a party other than the enterprise; receiving from the third-party GAI a response to the query; and presenting the response to a human user via a human interface device (HID).

The invention provides a software development project progress prediction management method based on artificial intelligence, and the method comprises the steps: obtaining a project module dependency graph, analyzing the dependency intensity and influence range between modules, and determining the weight of a key module and the weight of a dependency module; according to the skill level of developers, assigning task priorities and combining technical complexity, calculating development time estimation of each module, and generating an initial progress plan; the development progress of the key module is monitored in real time, if lagging is found, a resource allocation mechanism is triggered, the task priority is evaluated again, and distribution of developers is adjusted; and if the deviation between the output of the progress prediction model and the actual situation exceeds a preset threshold value, triggering a model self-learning mechanism, retraining the progress prediction model, and updating the Bayesian network and the adaptive filtering parameters.

Methods, apparatuses, or computer program products provide for outputting software development insight components to a software development insight interface. A multi-resource software development work graph structure associated with one or more development unit identifiers may be retrieved. The multi-resource software development work graph structure may be configured to represent relationships among a code pull request set and an issue object set that are associated with the one or more development unit identifiers. A software development context object associated with the one or more development unit identifiers may be received. One or more software development insight components may be determined by traversing the multi-resource software development work graph structure based on the software development context object. One or more software development insight components may be outputted for rendering to the software development insight interface.

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 for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

A method is provided for optimizing regression testing in a software development environment. The method includes analyzing source code to create a structural dependency graph that maps dependencies between code elements; identifying changes in the source code between a current version and a previous version; mapping the identified changes onto the structural dependency graph to determine affected code elements; selecting a subset of regression tests based on the affected code elements identified in the dependency graph; and executing the selected subset of regression tests to validate the changes in the source code.

The invention discloses an intelligent demand structuring method and an intelligent demand structuring system, which are used for generating a structured demand document which can be used for subsequent software development based on business demand description of a natural language. For the simple requirement of a single module, one sentence input is adopted; for complex demands of multiple modules, a business modeling agent is called to carry out modeling understanding, natural language demands are converted into a business concept set, and the business concept set is further decomposed into specific structured demands by a demand structured agent. In the whole process, the user can adjust and refine the business software demand in real time through interaction with the business modeling agent and the demand structuring agent. According to the method, the structured demand document capable of being used for subsequent software development can be completed through natural language description, an efficient, flexible and intelligent new mode is provided for the structured demand of multi-field personalized service software and even software development, and the efficiency and quality of software development can be greatly improved.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The invention relates to a method and a device for generating software based on a large model technology, a medium and electronic equipment, and relates to the technical field of software generation, and the method comprises the following steps: acquiring demand information of a user for software generation; generating a configuration definition file corresponding to the large model generation demand information through a preset code, wherein the configuration definition file is a configuration file of a domain-specific language definition device; performing regular format reformatting processing on the configuration definition file to obtain a processed file; based on the processed file, determining a service flow logic corresponding to a service function in the to-be-generated software, and based on the service flow logic, generating a target software code corresponding to the to-be-generated software through a code generation large model, the format of the target software code being a configuration file format of the domain-specific language definition device; and based on the target software code, through a preset rendering engine, generating a front-end page and a rear-end page of the software needing to be generated. The method has the effect of improving the software development efficiency.

Disclosed herein are system, method and / or computer program product embodiments, and / or combinations and sub-combinations thereof for providing a platform for developing an application that interacts with a set of IoT devices. An embodiment provides a source code editor that enables a user to generate source code of the application and an application builder that generates an executable version of the application based at least on the generated source code. Generating the source code includes defining an input or output of the application as an event that is mappable to a first set of IoT devices in a first network of IoT devices when the application is installed thereto and to a second set of IoT devices in a second network of IoT devices when the application is installed thereto, the first set of IoT devices including at least one device type not included in the second set of IoT devices.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

In one embodiment, a method includes detecting a change in context of a computing system to a current context, changing a first registration status of a first set of actions with an assistant system associated with the computing system based on the change in context to the current context, where the first application is in a foreground of a user interface of the computing system, changing a second registration status of a second set of actions with the assistant system based on the change in context to the current context, where the second application is in a background of the user interface of the computing system, updating a list of registered actions for the assistant system based on the changed first and second registration statuses, and storing the updated list of registered actions on the computing system.

A method and computer-implemented process for identifying behavioral anomalies and risk factors is disclosed. The method includes (A) obtaining, by a risk assessment module, first data associated with activity information of a digital identity with respect to a digital development tool; (B) associating, by the risk assessment module, second data with a first risk tag based on a risk-anomaly matrix; (C) generating, by the risk assessment module, a first predicted anomaly using a machine learning module trained using a first training set, wherein the first training set comprises the second data and the first risk tag; (D) associating, by the risk assessment module, the first predicted anomaly with a second risk tag based on the risk-anomaly matrix; and, (E) transmitting, by the risk assessment module, a notification associated with at least one of (i) the anomaly and (ii) the predicted anomaly, and the digital identity.

The invention provides a conflict resolution method and system for realizing multi-person collaborative editing based on WebSocket and JSON-PATCH, and relates to the technical field of I T and software development, the method comprises the following steps: obtaining an editing operation of a user at a client of a low-code platform, and converting the editing operation into operation information in a JSON format; the method comprises the following steps: receiving operation information in a JSON format sent by a plurality of clients through a WebSocket server; storing the received operation information in the JSON format in a database according to a user account; based on the operation type defined by the JSON-PATCH, conflict detection is carried out on the concurrent operation of the multiple users; according to a preset priority rule, carrying out merging processing on the detected conflict operations; a result obtained after combination processing is broadcasted to all the clients through a WebSocket server; and the client receives the broadcast combination result and updates the low-code platform interface. According to the method, the user operation is formatted by using the JSON-PATCH, so that operation conversion is realized to solve conflicts.

The invention provides a legacy system-oriented large-model-driven API document automatic generation system, belongs to the crossing field of artificial intelligence and software development, and provides a multi-modal data fusion and closed-loop verification mechanism aiming at the defects of a traditional API document generation method in the aspects of semantic comprehension, dynamic context capture and multi-technology stack adaptation. A code static feature and a dynamic track during operation are analyzed through a multi-source data acquisition module, and an interface semantic feature is extracted in combination with a field self-adaptive large model of a semantic enhancement analysis module; deducing an implicit service rule by fusing static / dynamic characteristics through a graph neural network, and generating a standardized document conforming to an OpenAPI specification through a parameterized template generative adversarial network (PT-GAN); and finally, performing three-level verification and closed-loop optimization through a sandbox environment. The method supports a heterogeneous system of 16 programming languages such as Java / C + + / Python, interface version changes can be automatically recognized, document patches are generated, the problems of missing and outdated system documents and low maintenance efficiency are solved, and maintainability and integration efficiency of enterprise-level systems are remarkably improved.

Systems and methods provide a first deployment criterion for deploying modified decision engines. A first existing decision engine is accessed, as well as a first modified decision engine that includes rule data generated by an artificial intelligence model based on the first existing decision engine. A first difference between a first output and a first modified output is determined, where the first output is generated by the first existing decision engine and the first modified output is generated by the first modified decision engine. A first selected decision engine is deployed to process subsequent data items to produce subsequent outputs, based on whether the first difference satisfies first deployment criterion. When metric generated based on the subsequent outputs satisfies a criterion modification condition, the artificial intelligence model is used to generate a second deployment criterion, wherein a second selected rule-based decision engine is deployed based on the second deployment criterion.

Contribution requests to a code repository are analyzed with a machine learning model before publishing. The machine learning model can be trained with past metadata of the contributor. Metadata can be extracted from the requests to determine whether the request is atypical for the contributor via a risk score. Requests determined to be atypical can be flagged for action by a security manager. Realtime assessment of code contributions can increase overall software security in a software development context.

Methods and systems for generating a live digital document are provided. The method includes receiving a static document file including human-readable data. Then, parsing the static document file into a plurality of subunits. Then, generating a sharable document splice of the static document file. Hie sharable document splice includes access to a subset of the plurality of subunits, and the access is provided through an Application Programming Interface (API) or Software Development Kit (SDK) endpoint for each subunit in the subset. Then, executing a digital thread script to generate the live digital document from the sharable document splice and an input digital model representation, using API or SDK endpoints in the sharable document splice. The live digital document is configured through the digital thread script to reflect changes in the input digital model representation. Finally, adding execution metadata for the execution of the digital thread script to the live digital document.