97 results about "Abstract syntax" patented technology

The present disclosure relates to a system and method for vulnerability localization based on deep learning, which comprises, at a minimum, a processor configured to: analyze a code file under detection to obtain a first abstract syntax tree devoid of semantic information; build upon the first abstract syntax tree by incorporating data-flow edges and / or control-flow edges, thereby forming a second abstract syntax tree with semantic-flow enhancement; split the second abstract syntax tree to obtain a plurality of second abstract syntax sub-trees; and input these second abstract syntax sub-trees into a pre-established vulnerability detection and localization model. Compared with existing code vulnerability detection methods, the present disclosure employs a semantically-enhanced abstract syntax tree and finely-grained segmentation thereof, enabling both the efficient detection and accurate localization of code vulnerabilities, characterized by swift detection rates, low false positive rates, and commendable interpretability of the detection results.

The invention discloses a self-adaptive SQL differential privacy noise adding method, and relates to the technical field of data protection. In order to accurately calculate and add noise of a proper magnitude in a complex SQL statement, the adopted scheme comprises the following steps: regularly collecting and updating metadata of a database, analyzing attribute features of a data table, and forming a data attribute feature library and a corresponding data sensitivity library; the SQL query request is rewritten into an abstract syntax tree and traversed, and an aggregation function, multi-table connection and a sub-query structure are recorded; key semantic information contained in the request is bound with the data sensitivity library, the sensitivity of the request is calculated, and a privacy budget is distributed in a self-adaptive mode; the privacy budget allowance is checked, when the allowance is sufficient, a data disturbance mechanism meeting-differential privacy is used for disturbing the query result, a disturbed result is obtained, and otherwise, the request is rejected. The method is used for accurately calculating the dynamic allocation privacy budget according to the characteristics of the complex SQL query.

Systems and methods for generating fixed function shared accelerators (FFSAs) implement and / or comprise operations of receiving source code, the source code indicating a plurality of workloads to be performed by an electronic circuit; generating a plurality of abstract syntax trees (ASTs) based on the source code, wherein respective ones of the plurality of ASTs include a plurality of nodes corresponding to function instructions; generating a plurality of fingerprinting vectors corresponding to the plurality of ASTs, wherein respective ones of the plurality of fingerprinting vectors encode at least one of a number of nodes, a number of edges, a density, a computation intensity, an operands percentage, a control, or a data dependency; and providing the plurality of fingerprinting vectors to a machine learning (ML) model, wherein the ML model is configured to predict similarities between different ones of the plurality of workloads and to output at least one candidate FFSA.

The invention discloses an intelligent contract vulnerability detection method based on a heterogeneous graph attention network. According to the method, firstly, the source code of the intelligent contract is preprocessed, the SCIR of the code of the intelligent contract is constructed, the complexity of the code of the contract is reduced, and vulnerability features are enriched; and constructing an intelligent contract code attribute graph SCPG based on SCIR, integrating various code graph structures such as an abstract syntax tree and a control flow graph, and comprehensively describing syntax and semantic features of the contract. And then constructing an intelligent contract code heterogeneous graph SCHG on the basis of the SCPG, optimizing code graph structure representation, and realizing high-quality modeling of node features. And finally, detecting the vulnerability of the smart contract by using a customized multi-layer heterogeneous graph attention network model MHGAN. The intelligent contract vulnerability detection method based on deep learning makes up for the defects of an existing intelligent contract vulnerability detection method based on deep learning, effectively improves the accuracy of intelligent contract vulnerability detection, and is excellent in the interpretability of the detection result.

The invention relates to the technical field of computers, and discloses a method for converting a C + + code into a CUDA-C code, and the method comprises the steps: receiving a C + + source code, carrying out lexical analysis and grammatical analysis, generating an abstract syntax tree, and carrying out semantic analysis to ensure that the source code is correct semantically; analyzing the abstract syntax tree, identifying a data dependency relationship among the calculation tasks, and determining loop statements and conditional statements in the source code; and converting a loop statement and a conditional statement in the source code into a kernel function, a thread block and a thread management code corresponding to the CUDA, and further generating a corresponding compilable source file. According to the method, the abstract syntax tree of the C + + code is analyzed, the parallelism in the abstract syntax tree is analyzed, the code is optimized, the CUDA-C efficient code is generated, then efficient automatic conversion of the code is achieved, and the method has great significance in accelerating program performance and shortening the development period.

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.

Validating code generated by artificial intelligence using abstract syntax trees includes generating, by an artificial intelligence (AI) language model, output source code based on input source code; determining an equivalency mapping between a first abstract syntax tree (AST) constructed for the input source code and a second AST constructed for the output source code; and indicating, based on the equivalency mapping, a validation result for the output source code.

In an embodiment, a computer generates a multi-sequence vector that contains a plurality of distinct sequences of distinct nodes of a parse tree of source logic. Based on the multi-sequence vector, the computer trains a logic encoder. After training and in a production environment, the logic encoder infers a fixed-size encoded logic from new source logic. Based on the fixed-size encoded logic, the new source logic is detected as anomalous by an anomaly detector. Both of the logic encoder and the anomaly detector are machine learning models and, herein, they may be separately trained. In an embodiment, the logic encoder is based on a natural language processing (NLP) language model architecture such as bidirectional encoder representations from transformers (BERT), or novel training herein may be self-supervised according to skip-gram for use with an unlabeled training corpus.

The invention discloses a similarity comparison-based large model supply chain automatic restoration method and device, and the method comprises the steps: firstly carrying out the semantic coding of an original vulnerability code through a pre-training model, and constructing a semantic and structure parallel dual-channel representation in combination with an abstract syntax tree and other program structures; cWE type intelligent classification is performed on vulnerabilities by using a locally deployed large language model subjected to LoRA fine tuning, and meanwhile, a zero sample semantic matching mechanism is introduced, so that the recognition capability of unknown vulnerability types is improved. And searching the closest historical case from the knowledge base through similarity vector comparison, and extracting a repair abstract to construct a model to generate a prompt. Patch codes and repair instructions are generated through a large model, automatic filing and knowledge base updating are supported, and the continuously-enhanced automatic repair capacity is achieved. The method can be widely applied to automatic vulnerability repair scenes of supply chain components such as large model plug-ins, code interfaces and dependent packages, and the safety, functionality and interpretability of code repair patches are greatly improved.

Some embodiments construct a set of build dependencies for a program without a full set of build instructions. The build dependency set is constructed without piggy-backing on a build process that would produce an executable version of the program. Representations of the program's structure, such as expression types, call targets, symbol tables, abstract syntax trees, and other internal compiler data structures, are emitted to persistent non-volatile storage instead of being used only as intermediate steps for executable code generation. Security analysis can then utilize the program representations. Licensing analysis can also utilize the dependency set to identify program components and their storage locations.

A code generator controller is configured to receive a plurality of test models and generate a set of source code files based on code templates. Moreover, the test models cover all possible code templates. Moreover, the code generator controller is further configured to generate an annotation file comprising annotations for each generated set of source code files. The annotations indicate which templates were used to produce the computer code instructions generate an annotation abstract syntax tree for each annotation file generate a code abstract syntax tree for each generated set of source code files and generate one superior abstract syntax tree structure representing all possible outputs for the code generator based on the code abstract syntax trees and the annotation abstract syntax tree.

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 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.

A computing device (800) configured to: receive (701) a database query (302) in a natural language and database information (303) comprising a schema; receive (702) a preliminary query (307) in structured query language; select (703) schema elements; select (704) examples each comprising a respective pair of natural language and structured query language queries, in dependence on respective structural similarities between the preliminary query and the structured query language query of a respective pair represented as normalized abstract syntax trees; form (705) a prompt (312) based on the selected schema elements and examples; and input (706) the prompt to a large language model (313) to form an output query (314) in structured query language. This may allow output queries to be formed using examples in the prompt that have semantic similarity with respect to the input query and the expected SQL, with the ability to support new domains with limited data.

The invention discloses a homework correction method based on image recognition, relates to the technical field of homework correction, and comprises the following steps: breaking through the limitation of the existing homework correction technology on surface processing of consecutive characters, and bringing consecutive deformation into an expression intention analysis framework through linkage of stroke topological characteristics and semantic compensation; metaphor words intensified by students deliberately, such as a s with lengthened strokes, can be recognized, and the rhetorical structure is prevented from being misjudged as word use errors; the method comprises the following steps of: forcibly merging and eliminating the segmentation of physical paging on a long rhetorical sentence through a cross-row syntax tree, and ensuring that a not only... but also type emphasis sentence is completely analyzed; a dynamic visualization mechanism is adopted to convert an abstract grammar rule into a perceptible scene: a time axis animation of a tense error visually presents dislocation of an action and a time node, and a depth rhetorical analysis triggered by a hand-drawn wavy line accurately marks a metaphor ontology-metaphor body mapping relationship, so that students can understand the essence of the error at a glance.

Some embodiments construct a set of build dependencies for a program without access to a full set of build instructions. When multiple clashing name resolutions are identified for a particular dependency, a union of the alternative versions is formed. Intermediate representations of the union of program versions, such as symbol tables, abstract syntax trees, and other internal compiler data structures, are emitted to persistent non-volatile storage, instead of using a single resolution to create temporary intermediate data to build an executable program. Security analysis and licensing analysis utilize the persisted program representations to analyze the union of multiple overlapping but different versions of the program.

A structured query statement similarity detection method, a device and a medium are provided. The method includes: acquiring first and second structured query statements, and constructing corresponding first and second abstract syntax trees, a correlation degree between the root node in the abstract syntax tree and the query result of the statement being higher than a correlation degree between other nodes and the query result of the statement; traversing a node in the first abstract syntax tree, matching the traversed node with a node at a same level in the second abstract syntax tree, and obtaining a matching result of the node; and determining a statement similarity between the first structured query statement and the second structured query statement based on a matching result of a root node in the first abstract syntax tree, a matching result of a child node of the root node, and their corresponding weights.

The invention discloses a memory management method and device, electronic equipment and a computer storage medium, and belongs to the field of data communication and software engineering. The method comprises the following steps: acquiring an abstract syntax mark ASN file, and detecting a static structure array element in the ASN file; replacing the static structure array element with a dynamic pointer element, and adding a dynamic memory mark for the dynamic pointer element; when the ASN file is converted into a C / C + + library file, converting the dynamic pointer element to generate a pointer data structure; during encoding and decoding, the dynamic memory is allocated for the pointer data structure according to the dynamic memory mark, the C / C + + library file generated by the ASN file is fully automatically optimized, the structural body array is converted into the pointer data structure, the memory space of only one pointer can be allocated for the pointer data structure, and compared with the allocation of the fixed memory space, the occupation of the static memory is greatly reduced; the problems that the data structure in the C / C + + code generated by the ASN is redundant and a large amount of memory is occupied are solved.

The invention provides an intelligent contract vulnerability detection method based on a graph neural network, and the method comprises the steps: extracting a program control flow diagram and an abstract syntax diagram from an intelligent contract code, inputting the program control flow diagram and the abstract syntax diagram into the graph neural network, converting the program control flow diagram and the abstract syntax diagram into high-dimensional feature vectors, and carrying out the classification. The intelligent contract vulnerability detection taking the contract source code and the byte code as input is realized; for a contract without a source code deployed on a block chain, intelligent contract vulnerability detection only for an intelligent contract EVM byte code is realized; compared with a traditional static analysis method, one of the biggest advantages is that the operation speed is high, and the method has high detection efficiency under the condition that the detection performance is guaranteed; meanwhile, other existing methods often only research the condition of detecting a single vulnerability, and on the basis of the method, multiple vulnerabilities can be detected at the same time, and good vulnerability detection performance is achieved only through byte codes.

The present invention belongs to the field of blockchain security technology, and discloses a smart contract security detection method, system, device, terminal and application. The smart contract security detection method includes: using open source code to train a word2vec model; encapsulating the open source code into a smart contract function according to the smart contract syntax; converting the encapsulated function into an abstract syntax tree, and extracting data flow and control flow information; converting the data flow and control flow information of the smart contract into a graph model; using the trained word2vec model to convert the graph nodes into vectors; using a graph neural network to train the graph model; reading out all node information, converting the smart contract function graph model into a vector; and using a classification model to determine whether the function vector contains smart contract vulnerability information. The present invention improves the efficiency of smart contract security detection and achieves good results.

The present invention provides a method for supporting the deep integration of MBSE modeling tools and modeling methodology, including: step 1: designing a methodology metamodel based on GOPPRR‑E meta-metamodel; step 2: defining a methodology mapping specification: a mapping diagram of GOPPRR‑E meta-metamodel and XML based on a meta-object mechanism; step 3: constructing a methodology model: loading the methodology metamodel stored in XML format into the MBSE modeling tool, and converting the methodology metamodel in XML into an internal model structure recognizable by the modeling tool by parsing the mapping specification; step 4: designing a physical exchange specification: designing a navigation modeling language based on XML, and designing and defining an abstract syntax. The present invention can solve the problems of poor scalability of existing methodologies, insufficient deep integration of methodologies and tools, and realize the design of a process model-driven system based on methodology construction.

The present invention discloses a homework grading method based on image recognition, which relates to the technical field of homework grading. The present invention comprises the following steps: breaking through the surface processing limitations of existing homework grading technologies on cursive characters, integrating cursive deformation into an expression intention analysis framework through the linkage of stroke topological features and semantic compensation, and being able to identify metaphors deliberately emphasized by students, such as "as" with elongated strokes, to avoid misjudging rhetorical structures as word errors; eliminating the fragmentation of long rhetorical sentences by physical paging through forced merging of cross-line syntax trees, ensuring complete parsing of emphasized sentences such as "not only...but also"; and converting abstract grammatical rules into perceptible scenarios through a dynamic visualization mechanism: a timeline animation of tense errors intuitively presents the misalignment of actions and time nodes, and in-depth rhetorical analysis triggered by hand-drawn wavy lines accurately marks the metaphor subject-metaphor mapping relationship, enabling students to understand the essence of the error at a glance.

Various aspects of the present disclosure relate to a User Equipment (UE) configured to or operable to generate a first message according to an Abstract Syntax Notation One (ASN.1) module and one or more ASN.1 sub-modules, wherein the ASN.1 module comprises information irrespective of features applicable to the UE, and wherein the one or more ASN.1 sub-modules comprises information respective to one or more features applicable to the UE, transmit, to a network entity, the first message, wherein the first message includes information respective to the one or more features applicable to the UE and excludes other information respective to one or more features inapplicable to the UE and receive, from the network entity, a second message based at least in part on the transmitted first message.

This invention discloses a code security scanning method, comprising: project file filtering: filtering project files in a source code repository according to a preset filtering strategy to generate a set of files to be scanned; scan scheduling: scheduling a first scanning tool and / or a second scanning tool to perform code security scanning on the set of files to be scanned according to the scan mode selected by the user, and obtaining a set of scan alert results, wherein the first scanning tool is a static scanning tool based on regular expressions or abstract syntax trees, and the second scanning tool is a deep scanning tool based on graph analysis; result standardization: deduplicating the scan alert result set to obtain a standardized scan alert dataset; report generation: generating a standardized scan report based on the standardized scan alert dataset. By scheduling the corresponding scanning tool to perform code security scanning on the files to be scanned according to the scan mode selected by the user, multiple scanning modes are provided to improve scanning accuracy.

The invention discloses a syntax tree structure feature-based service code version difference identification method and system, and belongs to the technical field of computers, and the key points of the technical scheme are that the method comprises the following steps: obtaining a first syntax tree and a second syntax tree, the first syntax tree and the second syntax tree are abstract syntax trees corresponding to two service codes needing difference comparison; dividing the first syntax tree into a plurality of node clusters according to the structural features of the first syntax tree; identifying difference nodes in the second syntax tree according to the function, hierarchical relationship and data input node corresponding to each father node in the plurality of node clusters, dividing the syntax tree into the plurality of node clusters, performing difference identification according to each father node in the node clusters, and further selecting comparison nodes according to the father nodes. That is to say, the comparison range is further reduced, finally the difference nodes are identified according to the comparison nodes, the limitation of global comparison is avoided, and the difference nodes are accurately determined while the comparison amount is reduced.

A structured query statement similarity detection method, a device and a medium are provided. The method includes: acquiring first and second structured query statements, and constructing corresponding first and second abstract syntax trees, a correlation degree between the root node in the abstract syntax tree and the query result of the statement being higher than a correlation degree between other nodes and the query result of the statement; traversing a node in the first abstract syntax tree, matching the traversed node with a node at a same level in the second abstract syntax tree, and obtaining a matching result of the node; and determining a statement similarity between the first structured query statement and the second structured query statement based on a matching result of a root node in the first abstract syntax tree, a matching result of a child node of the root node, and their corresponding weights.

The present invention discloses a software vulnerability detection method based on fuzzy testing, which relates to the technical field of software vulnerability detection, and includes the following steps: S1: obtaining software source code, and preprocessing the obtained software source code, so that the preprocessed source code has generalization, thereby improving the accuracy of the detection result when the source code is subsequently detected for vulnerabilities. The present invention proposes a software vulnerability detection method based on fuzzy testing, which normalizes the source code file to eliminate the deviation caused by the programming habits of the code writers in the software source code file when the software source code is subsequently detected for vulnerabilities, and performs feature representation processing on the source code file through an abstract syntax tree and a program dependency graph, so as to more comprehensively characterize the semantic features of the source code, and enable the constructed software vulnerability detection model to set different noise thresholds based on the noise content in different input source code files.

The present invention discloses a relation-enhanced retrieval method, device and readable medium based on a graph database. The method obtains a natural language question and performs word segmentation processing to obtain a word sequence, and uses a pre-trained language model to generate word vector features of the words in the question, word vector features of table names and word vector features of column names; a graph database is constructed based on the word vector features of the table names and the word vector features of the column names, and a self-attention mechanism is used to generate deep relation fusion features based on the word vector features of the words in the question and the graph database; the word vector features of the words in the question, the word vector features of the table names, the word vector features of the column names and the deep relation fusion features are spliced ​​to obtain a joint feature vector, and the joint feature vector is input into a trained Text2AQL model to obtain a syntax tree. According to the syntax tree, an AQL query statement is obtained by using the abstract syntax description language specification mapping, which can reduce the difficulty of interaction between users and the graph database and improve the user experience.

The present disclosure relates to a unit testing method, device and storage medium. The present disclosure generates an abstract syntax tree by parsing the source code of a simulation object and the source code for generating a proxy class in a compilation phase; adds a first statement in the abstract syntax tree by using an annotation processor, and uses the first statement to switch the associated class of the simulation object including non-executable attributes to the proxy class including executable attributes corresponding to the simulation object, thereby realizing the use of the proxy class to replace the associated class of the simulation object to participate in the unit test, so that the test including non-executable attributes is completed without modifying the source code of the simulation object, thereby improving the unit testing efficiency.

The application provides a private intelligent code collaboration optimization method and system based on graph search, and relates to the technical field of intelligent auxiliary tools for software engineering, which comprises the following steps: obtaining source code files and running log data of a private project; performing syntax tree analysis on the source code files through a Tree-sitter parser to generate corresponding abstract syntax trees; constructing a code knowledge graph by analyzing function call relationships, inheritance relationships and module dependency relationships; monitoring changes in project files in the code knowledge graph through a code change event processor and updating the code knowledge graph in real time when the files are changed; establishing an association relationship between the running log data and corresponding function nodes in the code knowledge graph; parsing natural language questions of developers into Cypher query statements through an intelligent Agent; obtaining query results from a graph database; and performing format processing on the query results to generate final results.