286 results about "Code snippet" patented technology

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 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.

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 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 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.

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.

Method for prioritizing code violations in a computer program, using machine learning includes: analyzing the computer program for code violations; extracting code snippets containing violations from the computer program; training a machine learning model to differentiate between vulnerable and non-vulnerable code in the extracted code snippets; inputting the extracted code snippets to a trained machine learning model to assign a vulnerability probability score to each snippet, wherein each vulnerability probability score indicates a severity of the violation for a respective snippet; ranking the code snippets based on their respective vulnerability probability score, wherein a higher score indicates a higher likelihood of causing severe vulnerabilities; and displaying the ranked code snippets to be fixed for their code violations.

The invention discloses a code generation method and device based on a large model, equipment, a medium and a product, and the method comprises the steps: carrying out the code generation through a first code large model according to target code demand data, and obtaining an initial code; according to the first code snippet, generating a test code through a second code large model to obtain a unit test code; according to the unit test code and the initial code, code repair based on editing distance regularization is conducted through a third code large model, and a to-be-analyzed code is obtained; performing iterative optimization on the to-be-analyzed code and the unit test code to obtain a target code corresponding to the target code demand data; according to the method, code generation, unit test generation and code repair are carried out in a multi-model cooperation mode, the reliability and accuracy of code generation can be effectively improved, software security vulnerabilities are reduced, and therefore the stability and security of a software system are improved.

The invention relates to the technical field of computer systems, in particular to a computer software analysis system, which comprises a dependency construction module, which is used for extracting key fields and checking function interface association when source code records are compared on the basis of debugging breakpoints and program component information, reviewing version differences and identifying conflict references, and establishing a database; invalid calls are screened through error mark comparison, a function call chain and a version label are integrated, a code snippet reference index is constructed, and a dependency path index is generated. According to the method and the device, when source code records are compared, key field extraction and function interface checking are introduced, version differences are examined, conflict references are identified, and invalid calls are screened, so that the software analysis process is more accurate, and the accuracy of fault diagnosis is improved. In the function calling process, the number of times of cross calling is counted, repeated path sources are checked, interleaved annular calling can be rapidly positioned, the deviation position of an error segment is defined, and repeated influences of cyclic calling on software performance are avoided.

The invention provides a code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information to obtain target business knowledge, and then the code development platform splices the input information, the context information and the target business knowledge according to a prompt template to obtain prompt information, then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and a code snippet generated by LLM reasoning is presented to a user. The LLM is prompted by using the prompt information of the same data source in different processes, complete sharing and complete alignment of the prompt information are realized, the accuracy of single code generation is improved, and context information and business knowledge are assembled through a unified prompt template, so that code snippets generated by the LLM are more accurate. Therefore, the code acceptance rate in the professional field is improved.

Embodiments of the present disclosure provide a code generation method, a device, and a storage medium. In the embodiments of the present disclosure, a custom retrieval library can be constructed, wherein the custom retrieval library comprises sub-snippets segmented from reference code snippets; on this basis, a target sub-snippet meeting a preset similarity requirement can be retrieved for an uncompleted code from the custom retrieval library; and then a code context of the target sub-snippet can be acquired to generate a retrieval result. Since the custom retrieval library is at the sub-snippet level, the code diversity in the custom retrieval library can be effectively enhanced, and various code variants can be covered, so that the retrieval precision can be effectively improved, effectively improving the quality of retrieval results. Moreover, prompt information can be constructed on the basis of the generated retrieval result and input into a code generation model.

The invention relates to the technical field of software security, in particular to a vulnerability mining system and device based on source code similarity. The vulnerability mining system comprises a vulnerability feature library which comprises vulnerability code snippets and feature information associated with the vulnerability code snippets; the coarse granularity positioning module is used for matching the vulnerability code snippets in the vulnerability feature library with the to-be-analyzed source code aiming at the to-be-analyzed source code, determining suspicious code snippets and forming similar code pairs; the feature representation module is used for acquiring feature information of the suspicious code snippets; the fine granularity positioning module is used for obtaining the semantic similarity, the structural similarity and the subgraph matching degree of the similar code pair according to the feature information associated with the two code snippets in the similar code pair, and determining the comprehensive similarity of the similar code pair; and the judgment module is used for determining whether the suspicious code snippets in the similar code pairs have vulnerabilities or not according to the comprehensive similarity. The method has the beneficial effects that the processing efficiency can be improved while the detection precision can be ensured.

The invention discloses an automatic driving test scene generation method based on a large-scale language model and a probabilistic scene generation language. The automatic driving test scene generation method comprises the following steps: S1, analyzing a natural language instruction input by a user by using the large-scale language model; s2, performing structured decomposition on the scene description; s3, generating a code snippet of a probabilistic scene generation language; and S4, assembling and compiling a script of the probabilistic scene generation language. The invention further discloses an automatic driving test scene generation device based on the large language model and the probabilistic scene generation language. According to the method, direct, efficient and reliable conversion from the natural language intention to the complex dynamic simulation scene is realized, and the method can be widely applied to the technical field of simulation testing and artificial intelligence.

A computer implemented method processes vulnerabilities. A processor set identifies a vulnerability and a description of the vulnerability in a vulnerability report. The processor set extracts a set of key exploit conditions for the vulnerability from the description of the vulnerability. The processor set generates a set of different code snippets for the set of key exploit conditions. The processor set searches a set of repositories for a match between the set of components and the set of different code snippets to form a search result. The processor set identifies vulnerability assessment information relating to an applicability of the vulnerability in response to the match between the set of components and the set of different code snippets in the search result.

The invention discloses a position and knowledge similarity-based software crash error repair scheme recommendation method, which outputs a repair method for error codes according to generated crash stack tracking information and corresponding codes when software crashes, and can help developers to quickly and efficiently solve the problem of software crash. When the method works, firstly, a context knowledge base is constructed, problem posts containing crash stacks on Stack Overflow are preprocessed, a knowledge base and an abnormal dictionary are constructed, and a language model is trained; thirdly, a related question set is preliminarily obtained through stack tracking, the comprehensive similarity of the question and an input stack is calculated through the position related similarity and the additional knowledge similarity, and the most related question and answer set is returned; and finally, performing relevancy calculation on the answer paragraphs based on multiple features, selecting paragraphs related to the crash reason by applying an MMR algorithm, and outputting root cause analysis and repair code snippets related to the crash error.

The invention discloses a code retrieval method, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: encoding a query text through a pre-training text encoder to obtain a query vector; a target vector related to the query vector is queried in a shared vector space, the shared vector space is a shared space of a pre-training text encoder and a pre-training code encoder, and the shared vector space comprises a plurality of code snippet vectors obtained by encoding the full-amount code through the pre-training code encoder; and determining a target code corresponding to the query text according to the target vector. According to the method and the device, the retrieval result can accurately reflect the user demand, and the retrieval efficiency, precision and recall rate are improved.

A method of generating a code output in response to a natural language problem description. The method includes: receiving the natural language problem description; generating, by a neural network based language model, a first candidate code snippet based on a first input prompt combining the natural language problem description and a first instruction; executing, at a code execution environment, the first candidate code snippet based on a unit test thereby producing a first feedback reflecting a correctness of the first candidate code snippet; generating, by the neural network based language model, a second candidate code snippet based on a second input prompt combining the natural language problem description, the first candidate code snippet, and the first feedback; and executing, at the code execution environment, the second candidate code snippet based on a runtime test thereby producing a second feedback reflecting a runtime efficiency of the second candidate code snippet.

The invention belongs to the field of deep learning, and discloses an adversarial sample generation method for improving adversarial robustness for a code model, which comprises the following steps: S1, acquiring a code snippet subjected to security repair, converting the code snippet into an abstract syntax tree, acquiring nodes and edges from the abstract syntax tree, and constructing a graph structure; s2, scoring each leaf node by using an improved GNN model, taking the first K leaf nodes with the highest score as high-influence nodes, respectively obtaining n replacement candidate nodes corresponding to each high-influence node, and finally obtaining nK adversarial samples; and S3, inputting the generated confrontation sample into the scoring model, if the confrontation sample can successfully attack the scoring model, rewarding the improved GNN model, otherwise, rewarding the scoring model. According to the method, dependence on gradient information is avoided, the application range of the adversarial sample generation method is greatly expanded, the method can play a role in more real scenes, and an end-to-end adversarial training closed loop is formed.

The invention discloses a code generation method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence application, the method comprises the steps that natural language information input by a target object is received, and the natural language information represents a code generation requirement of the target object described by a natural language; determining structured query information according to the natural language information and a preset template; querying a code database according to the natural language information and the structured query information to obtain a target code snippet; and inputting the target code snippet and the natural language information into a large language model to obtain a code generated by the large language model. The technical problem that codes generated by the large language model according to the natural language are inaccurate is solved, and the technical effect of improving the accuracy of the codes generated by the large language model according to the natural language is achieved.

The embodiment of the invention relates to the technical field of information, and discloses an interface verification and test method and device based on a large model, a medium and a product, and the method comprises the steps: obtaining a to-be-tested interface document, analyzing the interface document through a first large model, constructing a test request parameter based on a request parameter definition, acquiring a real-time response message by calling a real interface; performing semantic comparison on a response example in the document structure information and the real-time response message by using the second large model to obtain a corrected interface document; comparing the parameter constraint condition in the corrected interface document with the verification logic in the business logic code snippet by using a third model to generate a logic verification passing result; and on the basis of the corrected interface document and a logic verification passing result, a Mock rule and an automatic test case script are generated by utilizing the fourth model, so that the maintenance cost and the false alarm rate of the automatic test script are reduced, and the automatic test efficiency is improved.

One or more new coding instructions are generated using a language model (LM) prompted to perform one or more genetic operations on one or more seed coding instructions of an initial set of coding instruction-snippet pairs. One or more respective coding snippets are generated to implement the one or more new coding instructions using a LM prompted to generate coding snippets for the one or more new coding instructions. A generational set of coding instruction-snippet pairs comprising the initial set of coding instruction-snippet pairs and a new set of coding instruction-snippet pairs comprising the one or more new coding instructions and the one or more respective coding snippets is created.

A Decentralized Trusted Control Plan (D-TCP) to provide a means to define object behaviors without the need for coding on a decentralized computing network. The D-TCP implements pluggable logic through a multitude of decentralized registries with pre-defined code snippets stored on a decentralized network. The D-TCP links the applicable code selected from the registries to create desired functionality while maintaining platform security and providing economic incentives for fostering growth of the multitude of decentralized registries.

The invention discloses a method for improving code writing efficiency. The method comprises the following steps: S1, acquiring and preprocessing a user programming instruction sequence; s2, constructing a behavior feature model, and generating a semantic map sequence in combination with big data analysis; s3, analyzing a user code and a project structure, and extracting context features of the code; s4, based on the semantic map sequence and code context features, using Transform to predict a current position code sequence under grammar constraints, and generating candidate segments; s5, grading and sorting the candidate segments according to the context structure matching degree, the semantic similarity and the confidence coefficient of the behavior feature model; s6, presenting the candidate code snippet with the highest score to the user and recording the response behavior of the user; and S7, storing the candidate code snippets and user feedback data, and updating model parameters and generation logic in real time. According to the method, through behavior-driven modeling, context fusion prediction and a multi-factor dynamic scoring mechanism, the code writing efficiency and quality are remarkably improved.