414 results about "Code snippet" patented technology

The invention provides an intelligent vulnerability mining platform construction method and system based on a large model. The intelligent vulnerability mining platform systematically identifies and analyzes potential security risks, collects and identifies code snippets with the security risks, arranges the code snippets into risk code paragraphs, identifies target code paragraphs, determines known vulnerabilities, generates context information, captures key events in actual operation, and records real execution paths and state changes. The method comprises the steps of generating a dynamic execution track, simulating different paths, generating an attack vector path, predicting unknown vulnerabilities and credibility scores of the unknown vulnerabilities, testing prediction results of high-credibility scores, confirming and generating real vulnerability information, recording the information to a vulnerability knowledge base, and generating a security report containing vulnerability description, influence ranges, risk levels and repair suggestions. According to the technical scheme provided by the embodiment of the invention, the detection accuracy and efficiency of the security vulnerabilities in the software system are improved.

The present application provides a vulnerability remediation code identification method, comprising: acquiring commit information of open-source software, the commit information comprising a committed code snippet and text information associated with the code snippet; inputting the commit information into a first language model and performing identification to obtain function change information and test case information; inputting the function change information into a vulnerability remediation identification model to obtain a vulnerability remediation code snippet for a target vulnerability, and inputting the test case information into a vulnerability test case identification model to obtain a test case code snippet for the target vulnerability; and inputting the vulnerability remediation code snippet for the target vulnerability and the test case code snippet for the target vulnerability into a second language model and performing integration and identification to obtain an identification result. On the basis of notable characteristics in a test case for a vulnerability, the identification rate of vulnerability remediation code can be effectively increased. In this way, a user can discover potential vulnerabilities in advance, thus reducing security risks.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention discloses an intelligent risk identification and self-adaptive repair method, system and device for a software supply chain and a medium, belongs to the field of network security and automatic software engineering, and aims to solve the technical problem of how to accurately and comprehensively identify software code supply chain risks including code snippets. A reliable and efficient automatic closed-loop repair scheme is provided, and the technical defects that in the prior art, the software code supply chain recognition range is limited, the repair process is rigid and the reliability is low are overcome. Analyzing the declarative dependency; meanwhile, semantic traceability based on artificial intelligence is carried out on the code snippets, and a global software material list is generated; and performing intelligent mapping on the software components in the global software bill of materials and the vulnerability database to identify risks.

The invention relates to the technical field of metadata generation, and discloses a data platform metadata automatic generation method based on a large model, which comprises the following steps of: firstly, performing unified standardization and preliminary grammatical analysis on an original SQL (Structured Query Language) code to obtain a structured intermediate representation; on the basis, preliminary blood relationship analysis based on rules is carried out, and simple column references are quickly identified and processed. For complex expressions which are difficult to accurately analyze by a traditional method, code snippets and context information of the complex expressions are accurately extracted and submitted to a large language model for deep semantic understanding and complex blood relationship analysis. And finally, integrating the complex consanguinity analyzed by the large model with the initial consanguinity list to form a comprehensive and accurate field-level consanguinity, and further generating complete data platform metadata. In this way, the defect that a traditional analysis tool understands complex semantics is effectively overcome, and the accuracy and integrity of metadata generation are remarkably improved.

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.

Techniques for intelligently prompting an LLM to refactor code are disclosed. A code snippet is accessed. This code is identified as potentially comprising a reference to an out-of-compliance library. Context for the code snippet is generated. An LLM prompt is then built. This prompt will be fed to the LLM, and the prompt instructs the LLM to refactor the code snippet into modified code, which calls a compliant library. Output of the LLM is displayed. This output is based on the LLM operating in response to the LLM prompt. The output includes a proposed rewritten version of the code snippet.

The invention discloses an automatic code security optimization method based on a large language model, and belongs to the technical field of code security optimization. Acquiring large-scale programming data to establish a programming data set, establishing a primary training model, generating an initial code and establishing an initial code set; the predefined vulnerability mode set scans the initial code set, vulnerabilities in the initial code set are identified and defined as initial vulnerabilities, and the initial vulnerabilities are positioned and classified; static analysis is matched with a semantic discriminator to evaluate the code snippets to obtain a new generation strategy, and the primary generation strategy is adjusted to obtain an optimized training model; capturing potential vulnerabilities as training signals, and optimizing the optimization training model to obtain a continuous optimization training model; and establishing a vulnerability library and a repair strategy library, and optimizing vulnerability time and a repair algorithm by analyzing data of the vulnerability library and the repair strategy library. Potential vulnerabilities in generated codes can be automatically recognized and repaired, a large amount of manual intervention is not needed, and code generation safety is improved.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention provides a security code automatic generation method and system based on a full information theory and mechanism ambiguity, and the method comprises the steps: S1, carrying out the feature extraction of a grammar verification layer of an input demand, extracting the grammar features of a code through the topological features of an abstract syntax tree, and carrying out the formalized rule verification of the extracted grammar features; s2, constructing a semantic association model of a cross-code snippet through a semantic analysis layer, and forming a propagation path for tracking a data flow and a control flow; s3, in the pragmatic optimization layer, combining pragmatic constraints of the business scene, and generating a security policy through a dynamic utility function; and step S4, optimizing parameters and security policies of the dynamic utility function through knowledge federal evolution and a closed-loop feedback mechanism. According to the method, a deep collaboration mechanism of grammar, semantic and pragmatic three-dimensional information and a dynamic strategy generation model are reconstructed, security logic is converted from external attributes to endogenous cognitive competence, and the security, interpretability and adaptability of automatic code generation are improved.

In some implementations, the method may include receiving, by one or more agent applications, a query from a user. In addition, the method may include providing a dataframe to the one or more agent applications. The method may include appending a predetermined number of initial entries from the dataframe to a suffix of the query. Moreover, the method may include constructing a standardized prompt template, where the query is embedded within the standardized prompt template. Also, the method may include Channeling the prompt template to one or more Large Language Models (LLMs). Further, the method may include Utilizing a GPT API to generate a generated code snippet. In addition, the method may include Executing the generated code snippet to create a resulting output. The method may include Relaying the resulting output back to the user.

Automatic code generation for robotic process automation (RPA) that uses a cognitive artificial intelligence (AI) layer to automatically generate computer program code, text, or other items based on an input source is disclosed. Expressions and / or code snippets may be generated for RPA workflows that provide code in a programming language or convert code from one language to another. The input source may include natural language sentence(s), source document(s), pseudocode written by a user for a desired task, etc. These inputs may be used to modify an RPA workflow, generate a new RPA workflow, create a document describing a task, generate an application, etc.

The invention provides a code dynamic completion method for a large language model. The method comprises the following steps: constructing a mixed training data set; the method comprises the following steps of: initializing a Transform model architecture fused with probability space topological transformation; based on the mixed training data set, executing model training of collaborative optimization of the main target and the auxiliary target; in each step of decoding, the model calculates the probability distribution of the next token according to the current context; the generated probability distribution is restrained in an effective probability subspace through spherical probability mapping, and grammar error candidates are filtered in real time and the probability distribution is adjusted in combination with a tabu table mechanism; and dynamically selecting a sampling strategy according to the current decoding depth, sampling from the adjusted probability distribution to obtain a next code snippet, and finally generating a code completion suggestion conforming to abstract syntax tree rules and semantic constraints. According to the method, the probability distribution constraint and the taboo table mechanism are combined, the model can dynamically adjust the generated probability distribution, the more appropriate candidate token can obtain the higher probability, and therefore the accuracy of code completion is improved.

The invention discloses an AI-based vulnerability repair rule generation method and related equipment, and relates to the field of electric digital data processing. A computer device performs double analysis on a vulnerability code snippet and a vulnerability description text through a large language model, constructs double representation of an abstract syntax tree and a semantic graph, and performs cross-modal alignment on the semantic graph and the abstract syntax tree to obtain a vulnerability context relationship matrix. And the computer device traverses the abstract syntax tree, extracts vulnerability code feature vectors, and determines a plurality of repair modes according to vulnerability type identifiers. And the computer equipment calculates the similarity between the vulnerability code feature vector and a plurality of context constraint conditions, screens a target repair mode, performs feasibility verification on the target repair mode based on the vulnerability context relation matrix, and determines an optimal repair mode, thereby generating a repair rule. The double analysis and cross-modal alignment mode greatly improves the accuracy and reliability of vulnerability repair rule generation.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

The invention belongs to the technical field of code completion, and particularly relates to a code completion method based on combination of retrieval enhancement and multimodality, which comprises the following specific steps: constructing a code completion data set, constructing a code completion model, dividing the code completion data set and training the code completion model, and performing code completion by applying the code completion model. According to the method, the to-be-completed code is subjected to retrieval enhancement, the similar code snippets are retrieved to assist the to-be-completed code in completion, the external similarity of the code can be well utilized, and the code completion efficiency is improved. The path features from the leaf nodes of the abstract syntax tree to the nodes to be complemented are extracted, so that the syntax structure information on the path can be well utilized, and meanwhile, the problem of path explosion can be relieved to a certain extent.

The invention relates to the technical field of computer security, in particular to an embedded software reinforcement method based on a national cryptographic algorithm. The method comprises the following steps: generating a key pair by using SM2, constructing a code dependency graph in combination with spectral clustering, and optimizing key distribution; carrying out segmented encryption by adopting SM4 symmetric encryption, and identifying sensitive code snippets; the integrity of the embedded software is monitored and responded in real time through SM3 hash check and anomaly detection; analyzing vulnerability distribution based on random forest regression by using an SM4 encryption update package and dynamically adjusting an update frequency; and encrypting an operation log through SM3, and identifying a risk trend and generating an operation report by using a variational auto-encoder. According to the invention, through a plurality of technical means such as hierarchical encryption, real-time monitoring, dynamic key management and risk prediction, an efficient, dynamic and intelligent security reinforcement scheme is provided for embedded software, and the overall security and stability of the system are improved.

The invention discloses a method and system for establishing a software code and demand tracking relation, and relates to the computer and software engineering technology, and the method comprises the steps: translating a Chinese demand and a code annotation into English, and separating a code logic from an annotation text based on a specified rule; word embedding is carried out on the preprocessed Chinese requirements, the code logic and the annotation text to obtain embedded semantic vector representation; performing hierarchical mixed matching based on the obtained semantic vector representation so as to associate the demand with the code; and fusing the association results through configurable decision logic to determine a final matching result. According to the method provided by the embodiment of the invention, based on a multi-modal semantic alignment technology of the pre-training model, high-precision and robust tracking relation identification is realized by combining deep semantic association of the modeling demand text and the code snippets.

The invention provides an intelligent software development auxiliary system and a use method, and belongs to the technical field of computer software development. The system comprises a project demand analysis module which is used for obtaining and analyzing project demand data and generating feature vectors of demands; the code style optimization module is used for analyzing semantics and styles of the original codes, analyzing and normalizing the original code structures, and generating code structures and style specifications meeting project standards; the intelligent code completion module is used for analyzing context semantics of the codes after style optimization, generating code completion suggestions conforming to project standards in combination with project demand data, and outputting structured completion code snippets; the technology stack intelligent recommendation module automatically recommends an optimal technology stack for the project according to the project demand data and the optimized code structure; and the development behavior risk detection module monitors programming behaviors of developers in real time, captures abnormal operations, marks potential risks and generates operation optimization suggestions.

Techniques for implementing an AI threat modeling tool are disclosed. A static analysis tool is used to extract a candidate code snippet from a code repository. The candidate code snippet is identified as potentially being a security relevant code element. The static analysis tool generates additional context associated with the candidate code snippet. An LLM prompt is generated. This prompt is structured to include the candidate code snippet, the context, and a directive to assign a classification to the candidate code snippet. The classification includes a source classification, a sink classification, a sanitizer classification, or a flow step classification. The LLM operates on the prompt to generate output comprising a specific classification for the candidate code snippet. The output is formatted into a data extension file that is consumable by the static analysis tool.

The invention provides a code defect detection method and system based on a large model. The method comprises the following steps: generating an enhanced semantic understanding vector corresponding to an initial source code by utilizing a pre-trained large-scale language model in combination with an attention mechanism; identifying the logic error pattern to determine the defect type and position of the initial source code; the reinforcement learning algorithm selects an optimal repair path by simulating a plurality of repair paths and evaluating the effectiveness of each repair path, and generates a repair code snippet through the optimal repair path; replacing a corresponding part in the initial source code with the repair code snippet, and generating a target source code; performing quality scoring on the target source code based on the code quality evaluation model to obtain a quality feedback result; and outputting the target source code with the change annotation and the quality feedback result. According to the method and the device, the speed and the accuracy of finding and solving coding errors in the software development process are improved, and meanwhile, the quality of repaired codes is also ensured.

The invention discloses a code completion method and device, a storage medium and equipment, and the method comprises the steps: firstly extracting a target code context from a to-be-completed target code file, and extracting a target code snippet from the target code context; then extracting a target embedding vector of the target code snippet, and obtaining a candidate code snippet similar to the target code snippet from a pre-constructed code snippet candidate library based on the target embedding vector; and then, constructing prompt by utilizing the context of the target code and the candidate code snippets, and inputting the prompt into a code generation large language model to obtain a code-complemented target code file output by the model. According to the method, the candidate code snippets similar to the target code snippets are retrieved based on the code snippet candidate library constructed by all the code files contained in the IDE project where the target code file is located, so that the retrieval range is expanded, the retrieval efficiency and accuracy are improved, and the final code completion effect is improved.

The invention discloses a graph representation learning and retrieval method and system based on a code warehouse graph structure, and the method comprises the steps: constructing a heterogeneous code graph related to an original code, a newly-added code or a modified code, carrying out the graph representation learning of the heterogeneous code graph, generating a node embedding vector, carrying out the self-supervised learning through an intelligent model, and carrying out the retrieval of the node embedding vector. And outputting the updated node embedding vector. The method comprises the steps of detecting whether newly-added codes or modified codes exist or not in real time, only performing heterogeneous graph modification on the newly-added or modified codes, receiving a query request from a user, generating corresponding code snippets according to the request, checking whether the target code snippets meet grammar specifications or not, and outputting a query result. According to the method, the structural relationship between codes is fully modeled, a structure perception retrieval mode is realized, the method is superior to a traditional text matching scheme, and the code semantic understanding capability is improved; and large-scale code graph online query is supported, and the applicability is wider.

The invention provides a multi-source information fusion code generation method and a multi-source information fusion code generation system based on a large language model. Acquiring application programming interface information and code relation information corresponding to the to-be-complemented code from code files in a first code file path and a plurality of second code file paths; according to the application programming interface information and the code relation information, matching from a target code library to obtain an undetermined context code snippet; inputting the to-be-determined context code snippets into a code evaluation model to obtain target complemented code snippets output by the code evaluation model; and according to code input cursor position information corresponding to the to-be-completed code, obtaining a preorder code and a suffix code of the to-be-completed code, and according to the preorder code, the suffix code and the target completion code snippet, generating a code completion cue word. According to the method, a more accurate code completion result is obtained.

A method for automated control code generation and verification includes: receiving a natural language command, the natural language command being configured to instruct the large language model to output a code text that meets control requirements corresponding to the natural language command; performing matching retrieval on a vector database according to the natural language command to obtain a sample code snippet; obtaining API structured information corresponding to the sample code snippet from a knowledge graph database; generating an initial control code according to the sample code snippet and the API structured information; and performing a multi-level virtual operation verification on the initial control code in a software motion control system, and generating a target control code according to multi-level verification results confirmed multiple times by the user and the initial control code.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing transformed code snippets using a large language model. In particular, the disclosed systems can determine a code snippet from a code, for instance, based on user selection of the code snippet. The disclosed system can analyze the code and / or the code snippet to generate a prompt comprising context. The context contains information about the functionality of the code. The disclosed systems further use a large language model to analyze the code snippet and the prompt comprising the context and generate transformed code. The disclosed systems may provide the transformed code snippet to one or more devices.

The invention provides a code reuse method and device based on AI drive, equipment and a medium. When a code submission event is detected, a code scanning process is triggered, a code analysis module driven by AI carries out multi-dimensional analysis on submitted codes, quantitative evaluation is carried out on the basis of a preset reusability evaluation model, and code snippets meeting a reuse standard are stored in a code knowledge base with a semantic index structure; analyzing annotation semantics based on a natural language processing technology through an AI retrieval engine, calculating semantic similarity between demand description and code snippets in a knowledge base in combination with a deep learning model, and positioning the code snippets which are optimally matched; after code multiplexing execution, performing incremental modification on the multiplexing code snippets and automatically generating change records; and carrying out value layering on the code snippets based on the Pareto analysis principle by using frequency trend prediction. Through combination of the AI technology and code reuse, the development efficiency can be remarkably improved.

The invention relates to a test case generation method based on a multi-modal knowledge base and self-adaptive cue words, belongs to the technical field of software testing, and solves the problems of insufficient reuse of historical project data and expert knowledge, lack of knowledge in a large model professional field, prominent illusion problem, low adoption rate of generated test cases, low practicability and the like. The method comprises the steps of generating an original test demand set of a to-be-tested item; forming a test demand-code snippet / function set based on the software source code file and the original test demand set; generating a first-stage test tree graph based on each test demand point and the corresponding code snippet / function; forming a software test outline based on the test items, the test subitems and the corresponding test demand points and code snippets / functions; forming a second-stage test tree graph based on the first-stage test tree graph and the software test outline; and generating a test case based on the second-stage test tree graph and the software test outline. And the specialty, the accuracy and the interpretability of the test case are enhanced.

The invention discloses a code review method, system and device, a storage medium and a program product, and the method comprises the steps: cleaning a to-be-reviewed code snippet, and obtaining an incremental code snippet and position information; vectorizing the incremental code snippets; semantic query is conducted on the vectorized incremental code snippet data based on an ES database according to the cosine law, development rules of similar semantics are obtained and ranked from high to low according to similarity values, and a preset number of first several snippets are obtained; recombining cue words, wherein the recombining cue words comprise incremental code snippets and a preset number of development rules; and sending the recombined cue word to a general generative large model to obtain an examination suggestion. The code snippets to be examined are pushed to the gilab warehouse by a developer, and are cleaned by the gilab warehouse through webhook triggering. According to the review method, the whole process is automatic, the review efficiency is high, review can be conducted according to the development rules in the team, knowledge sharing is achieved, the review result is objective, and the problem that the text exceeds the upper limit is avoided.

The invention discloses a method for analyzing software code similarity and an analysis system, and relates to the field of software engineering and machine learning. The method comprises the steps of establishing a code snippet database, extracting multi-dimensional features of code snippets, executing similarity analysis of the code snippets, and establishing a similarity analysis result; performing context backtracking analysis on the code snippets in the database by using an adaptive analysis network, and establishing a context similarity result; performing static and dynamic fusion analysis on the database, establishing a joint function similarity, and establishing a function similarity result; code version change analysis is executed, and a version similarity result is established; and performing code similarity retrieval management according to the four analysis results. The technical problem that the code similarity analysis efficiency and precision are low due to the fact that the code snippets are high in complexity in multi-dimensional feature analysis is solved, and the code similarity analysis accuracy and efficiency are improved in the mode that multi-dimensional analysis and self-adaptive backtracking analysis are combined.