555 results about "Abstract syntax tree" patented technology

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention provides a private intelligent code collaborative optimization method and system based on graph retrieval, and relates to the technical field of software engineering intelligent auxiliary tools, and the method comprises the steps: obtaining a source code file and operation log data of a private project; the method comprises the following steps: performing syntax tree analysis on a source code file through a Tree-siter analyzer to generate a corresponding abstract syntax tree; constructing a code knowledge graph by analyzing a function call relationship, an inheritance relationship and a module dependency relationship; monitoring the change of a project file in the code knowledge graph through a code change event processor, and updating the code knowledge graph in real time when the file is changed; establishing an association relationship between the running log data and a corresponding function node in the code knowledge graph; the method comprises the following steps: analyzing a natural language problem of a developer into a Cypher query statement through an intelligent Agent; obtaining a query result from the graph database; and formatting the query result to generate a final result.

The invention provides a file uploading attack interception method based on semantic entropy enhancement, and aims at overcoming the defects of an existing file uploading security protection technology in the face of complex attacks. The method specifically comprises the steps that S1, file format analysis and content extraction are conducted, hidden scripts are mined through nested content recognition, and intermediate representation is generated through grammar cleaning and coding specifications; s2, constructing an abstract syntax tree and semantic entropy calculation, tracking a pollution chain, analyzing a high-risk function, identifying a high-entropy character string, modeling and controlling flow complexity, and generating a semantic entropy vector; s3, dynamic scoring and decision making are carried out, and accurate judgment is carried out in combination with white list perception, feature comparison, multi-modal model scoring, adaptive threshold and sandbox observation; and S4, carrying out real-time interception and feature synchronization, blocking malicious file landing, generating an attack log and synchronizing an attack fingerprint. The method takes the semantic entropy vector as a core, breaks through the limitation of static features, remarkably improves the recognition rate of complex attacks, reduces missed judgment, and guarantees the safety of Web applications.

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 multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

The invention belongs to the technical field of software security, and particularly discloses a warehouse-level vulnerability detection method and device based on a large model function semantic abstract. The method comprises the following steps: generating a vulnerability detection abstract syntax tree according to a to-be-detected source code; generating an assembly prompt word according to the complete source code of the node function in the unprocessed state and the target semantic abstract, and analyzing the node function in the unprocessed state according to the assembly prompt word by calling the target large model; and determining a vulnerability detection result of the to-be-detected source code according to the initial function semantic abstract and the structured function semantic abstract. Through the above mode, the vulnerability detection abstract syntax tree is generated by using the program structure modeling strategy and the full function call graph is constructed, and then the semantic abstract of the structured function is determined by using the semantic understanding capability of the target large model, that is, deep security analysis and vulnerability mining are performed from the local function to the global context. Therefore, the accuracy and comprehensiveness of warehouse-level vulnerability detection can be effectively improved.

The invention relates to the technical field of vulnerability repair, in particular to a source code vulnerability repair method, electronic equipment and a storage medium, and the method comprises the following steps: obtaining an abstract syntax tree and a control flow graph according to a source code containing a vulnerability, and generating a vulnerability context feature vector by using a graph neural network model in combination with vulnerability position information, determining a historical vulnerability repair case corresponding to the vulnerability context feature vector from a vulnerability-repair knowledge base, inputting the vulnerability context feature vector and the corresponding historical vulnerability repair case into a code generation model, outputting a candidate repair code set corresponding to the source code, evaluating each candidate repair code, and determining the vulnerability-repair knowledge base according to the candidate repair code set. Screening out an optimal repair code to automatically repair the source code vulnerability; according to the method, deep semantic analysis is performed on the vulnerability context, and intelligent reasoning is performed, so that the repair code with correct grammar and adaptive context can be generated, and the automation level and accuracy of vulnerability repair are remarkably improved.

The invention discloses a natural language driven CAD (Computer Aided Design) modeling method based on multi-agent collaboration. According to the method, a collaborative mechanism of a structure planning agent, a parameter completion agent and an execution verification agent is introduced, and the method comprises the steps that firstly, a structure planning agent analyzes natural language description and generates an abstract syntax tree structure meeting a CAD modeling rule; then, on the premise that the structure of the abstract syntax tree is kept unchanged, a parameter completion agent predicts and completes all symbol and numerical parameters, and a complete CAD modeling instruction sequence is generated; and finally, the execution verification agent performs structure and parameter dual verification on the generation instruction by using the geometric kernel, and feeds back and corrects if necessary. According to the method, a strategy of combining field adaptive full-parameter pre-training and task specific fine tuning is adopted in a training stage, and a structure-parameter decoupling modeling and closed loop verification mechanism is introduced, so that the structure accuracy, the parameter matching property and the geometric performability of a generated result are effectively improved.

The invention discloses an ANTLR4-based multi-manufacturer network equipment configuration analysis method and system, and the method comprises the steps: carrying out the preprocessing of a configuration file; based on a machine learning algorithm of multi-dimensional feature analysis, automatic identification of configuration file manufacturer types is realized; based on the identification result of the manufacturer type, loading an ANTLR4 grammar parser corresponding to the manufacturer type; a lexical analyzer in ANTLR4 realizes lexical analysis on the configuration file through lexical rule definition, a Token classification system and an optimized analysis algorithm; a syntactic analyzer in ANTLR4 constructs an abstract syntax tree according to a lexical analysis result and a syntactic rule defined based on a context-independent grammar; traversing the abstract syntax tree, and extracting key configuration information; and converting the specific configuration format of each manufacturer into a unified standard model. According to the method and the system, network equipment configuration files of different manufacturers can be automatically identified and analyzed, and key configuration information is extracted and converted into a unified model.

The invention belongs to the technical field of data statistics, and particularly discloses a Python-based dynamic SQL (Structured Query Language) generation and data statistics method and system, a terminal and a medium. The method comprises the following steps: acquiring statistical demand configuration in an Excel file; analyzing the Excel file to generate a structured data object, and carrying out semantic analysis and metadata consistency verification on the natural language description; constructing an SQL abstract syntax tree through a Python analysis module, and executing syntax mapping and function replacement according to a database adaptation rule; connecting a target database through a Python database driver and executing an SQL query statement; and after a query result is obtained, outputting an Excel file, a visual chart or interface data. According to the method, automatic analysis, SQL dynamic generation, safe execution and intelligent result output of data statistics requirements are realized.

The invention relates to the technical field of computer software development and databases, in particular to an embedded database programming method which comprises the steps of intention model construction: scanning source codes to identify embedded syntax blocks, constructing an abstract syntax tree, stripping a bottom driver and generating a driver-independent intention model; a logic sequence synthesis step: injecting a dialect parameter and a resource strategy, synthesizing and executing a logic sequence and generating a target code; a fingerprint verification step: intercepting a request during operation, calculating a structure fingerprint, and performing consistency verification on the structure fingerprint and the reference fingerprint; and a binding execution step: executing the request after the verification is passed, and establishing weak reference life cycle binding of the resource object and the host variable. The resource leakage risk is eliminated, and the robustness of the system under long-time operation is enhanced.

The invention relates to a cross-architecture code automatic migration method, system and device based on a large language model agent and a medium. The method comprises the following steps: acquiring a source code, a construction script and an architecture related header file of a target project; based on a pre-constructed transplantation target system architecture knowledge base, analyzing system architecture differences in combination with construction scripts and positioning architecture related positions needing to be modified in source codes to obtain a preliminary modification scheme; constructing scripts through static analysis and carrying out abstract syntax tree analysis on source codes to obtain a global dependency directed graph; based on a transplantation target system architecture knowledge base, performing migration code semantic matching on the source code according to the preliminary modification scheme and the global dependency directed graph, and generating a semantic equivalent replacement code to obtain a patch list; and sequentially executing incremental compiling, function consistency testing and performance regression verification on the migration project copy after the patch list is applied to generate verification information and a verification result. By adopting the method, cross-architecture code automatic migration can be realized.

The invention relates to the technical field of mobile application continuous integration automation, and discloses a multi-channel APP automatic packaging system which comprises the steps that byte code files in a released mobile terminal APP installation package are acquired, and an occupied symbol space set is generated; generating an available confusion namespace by using set complementary operation; extracting method-level code change by using an abstract syntax tree difference algorithm, and generating a change method set; generating a patch special confusion rule for the change method set by using a namespace allocation algorithm; compiling and obfuscating the change method set by applying a patch special obfuscating rule to generate a safe obfuscating patch byte code; performing packaging and signature operation on the safe obfuscation patch byte code, and outputting a minimized obfuscation safe hot repair patch pack; according to the method, the technical problems that class definition conflicts or method calling errors occur after the hot repair patch is loaded, and the downloading success rate of a user is affected due to the overlarge size of the hot repair patch are solved.

The invention provides a structured query statement generation method and device, equipment, a medium and a program product, and can be applied to the field of artificial intelligence and the field of financial science and technology. The method comprises the steps that in response to a natural language statement input by a user, the natural language statement is analyzed through a natural language processing model, and semantic elements are obtained; performing syntax analysis on the semantic elements to generate an abstract syntax tree; based on the abstract syntax tree, database structure metadata coding is conducted on the natural language statement through a deep learning model, and a first structured query statement is generated; and performing security verification on the first structured query statement to generate a second structured query statement.

The invention discloses a unit test code generation method, system and device and a medium, which are applied to the technical field of code generation, and comprise the following steps: performing program structure interface analysis and abstract syntax tree analysis on a target engineering code to obtain a structured meta-information set and a source code syntax structure model; generating a structured context feature vector based on the structured meta-information set and the source code grammar structure model, and retrieving a historical test case sample set from a historical test case database, wherein the global line coverage rate weighted similarity of the historical test case sample set and the structured context feature vector meets a matching condition; after Prompt template filling of different test subtasks is performed based on a historical test case sample set and structured context data through an Agent agent, different modular test codes are generated by adopting different test code generation models, and closed-loop verification is performed after the different modular test codes are integrated into unit test codes. Therefore, the code quality, practicability and adaptability are improved.

The invention discloses a C + + Mock class automatic generation method based on an inverse memory agent and a contrast knowledge graph, and aims to solve the problems that a virtual function coverage gap in a large C / C + + code library is difficult to find, the Mock code maintenance cost is high, and the automatic repair efficiency is low. According to the method, a difference context matrix is constructed by analyzing and merging request patch metadata, and an incremental abstract syntax tree is generated; generating a backtracking queue based on the event intensity and the semantic overlapping degree, and extracting a virtual function change list; the multi-scale difference extraction is completed in combination with an external expansion operator according to the rule that the path is not disassembled and the node is movable; a self-adaptive strategy table and a Mock specification are generated, through code synthesis and lightweight compilation verification, automatic self-healing is achieved through root cause positioning and minimum cost repair, finally, a result is written into a knowledge graph, and parameters are dynamically adjusted. According to the invention, Mock generation accuracy and version adaptability are improved, manual intervention is reduced, and test maintenance efficiency is improved.

The invention belongs to the technical field of computer data security, and particularly relates to a data contract-oriented strategy real-time compiling and consistency hot deployment method and system.The method comprises the steps that according to a strategy input by a user, strategy definition and standardized representation are based on formalized semantics, and a data contract-oriented strategy is compiled in real time; constructing a strategy abstract syntax tree with complete semantic information; inputting the abstract syntax tree into a multi-stage compilation optimization pipeline, and generating a high-performance intermediate representation (IR) code by using a cost model driven optimization decision mechanism; a policy distribution architecture based on a publishing-subscribing model is adopted, and an improved Gossip protocol and a Paxos atomic submission algorithm are combined to realize distributed consistency synchronization and real-time hot deployment of a policy; and efficient and safe execution of the strategy is realized through a register type micro virtual machine and a self-adaptive execution mode. The problems that in the prior art, execution performance has bottleneck, strategy updating is delayed, complex strategy supporting capacity is weak, and cluster strategy consistency is difficult to guarantee are solved.

The invention discloses a code similarity detection method and system based on a large language model, and the method comprises the steps: obtaining a to-be-detected source code pair, and marking the to-be-detected source code pair as a source code A and a source code B; performing code cleaning and format standardization on the source code A and the source code B, and mapping a variable name and a function name which are customized by a user into a uniform placeholder; analyzing the source codes A and B based on the abstract syntax tree, respectively replacing variable names and function names in the source codes A and B with unified serialized placeholders, and maintaining a mapping table; meanwhile, expanding a lexical dictionary of the pre-training large language model, and inserting a special identifier; splicing the replaced code snippets with special identifiers, and constructing a structure sensing input sequence; inputting the structure perception input sequence into a pre-trained large language model backbone network for feature coding to obtain a high-dimensional semantic feature vector containing global context information; connecting a multi-task prediction head behind the large language model backbone network, inputting the feature vector into the multi-task prediction head, outputting probability distribution of code clone types through a classification task head, and respectively outputting a row level similarity score and a lexical element level similarity score through a regression task head; and according to the classification probability and the regression score, performing comprehensive judgment by combining a preset threshold, and generating a detection report. According to the method, similar codes after variable renaming, statement rearrangement or control flow transformation can be accurately recognized, and the accuracy and robustness of code similarity detection are improved.

A computer-implemented technique for generating program code includes receiving a natural language description of a hardware module, generating a first plan based on the natural language description, extracting first circuit information from the natural language description, generating a second plan based on the first plan and the first circuit information, and generating first program code in a hardware description language based on the second plan.

Systems and methods are provided for attributing source code, including ingesting source code from a plurality of repositories, normalizing the ingested source code using abstract syntax trees (ASTs), and converting the normalized source code into vector embeddings that represent syntactic and semantic features of the source code. Unique fingerprints are generated from the vector embeddings, the fingerprints are compared against a vector database to identify potential matches and verify code attribution, and feedback and alternative code suggestions are provided based on the comparing the fingerprints.

The invention discloses a graph database query acceleration method and system. The method comprises the following steps: constructing panoramic query observation data through non-intrusive log interception and structured processing; the query statement is analyzed into an abstract syntax tree, semantic normalization processing is carried out, and a unique hash value is generated; predicting a hotspot query template and high-frequency parameters thereof based on a popularity value score model, and asynchronously preloading a result to a cache; dynamically calculating adaptive survival time for each cache item according to the data change frequency and the access popularity; and constructing a multi-level cache architecture comprising a local cache, a distributed cache and a database built-in cache, and performing intelligent routing and collaborative backfilling based on a hash value. According to the method, intelligent and transparent acceleration of graph database query is realized, the query performance is remarkably improved, the database load is reduced, and the problems of redundancy, high cold start delay and difficulty in balancing cache consistency caused by grammar difference in traditional cache are solved.

The invention provides a source database storage process migration method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grammar analysis of an original storage process in a source database, and generating a corresponding abstract syntax tree; based on a preset rewriting rule set, performing semantic equivalent transformation on the flow control syntax structure in the abstract syntax tree to generate a transformed abstract syntax tree; and generating a target script in a target execution environment from the converted abstract syntax tree, executing the target script in a script running engine, dynamically generating a standard SQL statement corresponding to the business logic defined by the original storage process, and submitting the standard SQL statement to one or more target data platforms for execution. And the business logic of the original storage process is reproduced. By introducing the target script as the intermediate execution layer, the flexibility of the migration scheme is greatly improved, so that the migration scheme is suitable for various big data platforms without the native storage process capability.

The invention relates to a cross-language software vulnerability detection method and device, and the method comprises the steps: carrying out the analysis of a Joern static analysis pair, carrying out the integration and semantic enhancement of an abstract syntax tree, a control flow graph and a data dependence graph, and obtaining a cross-warehouse heterogeneous code graph; obtaining cross-language intermediate representation based on a compiler framework; after the cross-language intermediate representation and the cross-warehouse heterogeneous code graph are modeled, weighted fusion is carried out through a gated cross attention mechanism, and a multi-modal data set is obtained; carrying out migration training on the multi-modal cross-language vulnerability detection model, and carrying out vulnerability detection on cross-language software to obtain a detection result; through multi-modal data fusion and modeling, in combination with cross-language intermediate representation and a cross-warehouse heterogeneous code graph, the defects of a traditional method in the aspects of cross-language generalization ability and context reasoning ability are effectively overcome; the method has the advantages that the generalization ability of cross-language vulnerability detection is improved, the false alarm rate and the missing report rate are reduced, and the comprehensive utilization effect of global structure information is enhanced.

According to the intelligent auxiliary learning method and system based on the model context protocol and the cognitive state modeling, decoupling of a model end and a tool end is achieved through the model context protocol, an error type-tool dependency topological graph is constructed through the cognitive state modeling, tool dynamic screening and pruning based on deterministic rule constraints are achieved, and the method and the system have the advantages that the method and the system are easy to implement. The Token consumption is reduced, and the accuracy of model reasoning is improved at the same time. In the aspect of safety control, semantic firewall middleware is introduced into the system, output streams are monitored in real time, illegal behaviors directly giving code answers are intercepted through text semantic analysis and code abstract syntax tree comparison, and a model is forced to turn to a thought guide mode. The system also includes hierarchical context compression to maintain long term memory, adaptive difficulty knowledge retrieval based on user cognitive states, and a mechanism to utilize code sandbox to assist verification of model reasoning logic correctness. According to the method, the behaviors of the large language model can be effectively regulated and controlled, and safe, efficient and personalized intelligent auxiliary learning is realized.

The invention discloses an intelligent detection method for network security vulnerabilities, and relates to the technical field of network security vulnerability detection, and the method comprises the following steps: analyzing source codes to construct an abstract syntax tree, a control flow graph and a call graph, identifying sensitive operation points based on a rule base, and screening hotspot functions in combination with code indexes and the call graph; extracting a candidate path from a program entry to a sensitive operation point, encoding to generate a path context snapshot, and scoring through a pre-training model; selecting a high-risk path to execute sparse symbolic execution, simplifying constraints by using variable interval information, and dynamically feeding back and adjusting scores; poC input verification is generated for the path triggering the vulnerability constraint, a report containing the path constraint, the triggering condition and the PoC is output, program analysis and machine learning are combined, the vulnerability detection efficiency and precision are improved, and the method is suitable for code security analysis of scenes such as an embedded system.

The embodiment of the invention provides a code review method and a computer program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing a to-be-reviewed code into an abstract syntax tree, extracting a dependency relationship of each code node from the abstract syntax tree, calculating the code complexity of each code node, generating a topological structure diagram according to each code node and each dependency relationship, marking each network node according to each code complexity and a preset code rule, and generating a topological structure diagram according to each network node. And performing visual review on the to-be-reviewed code. Therefore, the cognitive load and troubleshooting time of a developer for positioning the architecture level problem in a large-scale project are effectively reduced and shortened.

The invention discloses a script generation method and device, equipment, a storage medium and a product, which are applied to the technical field of computer data processing, and the method comprises the following steps: obtaining business demand information corresponding to a target business function; semantic analysis is conducted on the service demand information, a template configuration file is generated, and the template configuration file at least comprises service parameters needed for achieving the target service function, attribute information of the service parameters and logic rules corresponding to the service parameters; analyzing the logic rule to generate an abstract syntax tree corresponding to the target service function; and performing data integration on the service parameters, the attribute information of the service parameters and the abstract syntax tree to generate an executable script for realizing a target service function. According to the scheme, manual code writing and manual template configuration are not needed, and the script generation efficiency is improved.

Certain aspects of the present disclosure provide techniques for generating knowledge graphs from program source code. An example method generally includes receiving a source code definition of a workflow implemented in an application. The source code definition of the workflow is converted into an intermediate representation of the workflow, the intermediate representation comprising a condensed version of the source code definition. An abstract syntax tree representation of the workflow is generated based on the intermediate representation of the workflow. A structured file is generated by traversing the abstract syntax tree representation of the workflow, wherein the structured file comprises a definition of the workflow in a knowledge graph-specific language. The structured file is deployed to a knowledge graph execution engine.

The invention relates to the technical field of SQL (Structured Query Language) grammar conversion, and provides a general SQL grammar conversion method, device and equipment and a medium, the method comprises the following steps: performing lexical analysis on a source end SQL statement to obtain a lexical unit set; based on the type features and semantic attributes of the lexical unit set, determining parser combiners, and constructing a specific parser by infinitely combining the parser combiners; the specific resolver is used for conducting grammar analysis on the lexical unit set, a general abstract syntax tree is constructed through a mapping assembly, and nodes of the general abstract syntax tree correspond to the specific resolver one to one; and traversing the general abstract syntax tree, and generating a target end SQL statement according to an SQL syntax rule of a target end database. Through the technical scheme, any complex source end SQL can be accurately converted into the target end SQL.

The invention provides an autonomous controllable database intelligent adaptation method and system oriented to localization ecology, and belongs to the technical field of database middleware and cloud native resource management. When the request label is missing or wrong, JDBC metadata, a system function return value and system table structure features are collected through detection connection to generate database fingerprints, and the database type and version are recognized in the localized feature library. For an SQL execution request, parsing the SQL into an abstract syntax tree based on a unified syntax parser, rewriting nodes according to a dialect rule base to generate a target dialect SQL, and performing paging, identification column generation, identifier reference, transaction-related semantic verification, execution plan optimization and dynamic routing execution; and thermally loading from the plug-in warehouse when the driver is missing. And for the resource opening request, calling a Kubernetes Operator to automatically create a domestic database instance, and returning connection information. The system integrates a national cryptographic algorithm and behavior auditing, and realizes connection information protection and abnormal SQL detection.