1034results about "Reverse engineering" patented technology

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention discloses an intelligent Bug management method and platform based on an AI large model. The method comprises the steps that structured error codes, unstructured log texts and development communication records are collected; standardized Bug features are obtained after preprocessing; semantic matching is performed based on a CodeBERT large model to generate a historical similar Bug case set, and a problem code segment is analyzed and positioned in combination with an AST abstract syntax tree; according to the project technology stack features, the historical similar Bug case set and the problem code segment, generating a repair scheme including code modification suggestions, automatically generated repair code snippets and implementation risk assessment; constructing and optimizing a triple knowledge graph; and finally, associating the standardized Bug features, the problem code segment, the repair scheme and the knowledge graph to obtain a Bug diagnosis result, and outputting the Bug diagnosis result. According to the method, intelligent positioning and accurate repairing of the Bug are achieved, the average Bug solving time is remarkably shortened, and meanwhile automatic precipitation of development experience is achieved through the continuously optimized knowledge graph.

The invention relates to a code file rewriting method and device based on a large language model, and the method comprises the steps: analyzing a source code file, obtaining the structure of a source code, and enabling the structure of the source code to comprise the definition of each function and the position of each function code in the source code file; respectively inputting each function code into a large language model, and respectively processing according to each cue word to complete rewriting of each function code; and combining each rewritten function code corresponding to each position in the source code file to form a rewritten code file. According to the invention, a scheme for optimizing and rewriting the codes based on the large language model and the file level can be provided.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

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 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 code vulnerability detection method, a code vulnerability rule configuration method, a static single assignment SSA code acquisition method, devices corresponding to the methods, and electronic equipment. The code vulnerability detection method comprises the following steps: acquiring a plurality of source code files and code vulnerability rules of an application program; generating an abstract syntax tree of the source code file, and obtaining a function call relationship; and traversing the abstract syntax tree, and obtaining a cross-function risk propagation path according to the function call relationship and the code vulnerability rule. By the adoption of the processing mode, the function calling relation and the abstract syntax tree are fused to conduct cross-function code vulnerability detection, cross-function vulnerability detection of context sensitivity, flow sensitivity and domain sensitivity is achieved, and vulnerability false report and missing report are avoided; therefore, the accuracy and recall rate of vulnerability detection can be effectively improved.

The invention discloses a static and dynamic analysis-combined source code vulnerability detection method, which comprises the following steps of: performing static scanning on an input source code, and preliminarily positioning potential vulnerabilities according to a preset vulnerability rule base; executing program slicing based on the control flow diagram and the data flow diagram, and extracting a precise code subset related to the vulnerability; a large language model based on a Transform structure is utilized, and a dynamic verification attack case is automatically generated according to the vulnerability type and the slice code; executing the test case in a sandbox environment, monitoring program abnormal behaviors in real time, and confirming actual availability of vulnerabilities; and finally, synthesizing static and dynamic results to generate a multi-level vulnerability detection report. According to the method, the accuracy and coverage rate of vulnerability detection can be effectively improved, the false report and missing report rate is reduced, high automation and intelligence of the source code vulnerability detection process are realized, and the method has good applicability and popularization value.

In some examples, a code file in a development system is selected and the selected code file is segmented, using a language-specific parser, to create multiple parsed blocks. A block of the multiple parsed blocks is selected and a cryptographic hash function used to create a block signature that uniquely identifies the selected block. The block signature is compared to multiple signatures of blocks in third-party libraries. The multiple signatures are stored in an index. When the block signature matches one of the multiple signatures of blocks in third-party libraries, the index is used to determine a source file from which the selected block was copied. Vulnerabilities included in the source file and fixes to address the vulnerabilities are identified to enable a software developer to apply the fixes to the identified vulnerabilities.

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

The invention relates to code verification, in particular to a code and document consistency reasoning verification system based on a cross-modal logic atlas, which comprises an input layer for receiving source codes and software documents; the feature extraction layer is used for performing feature extraction on the source code and the software document; the abstraction layer is used for converting the extracted features into comparable intermediate representations; the unified modeling layer is used for mapping code logic and document semantics into a unified atlas structure and performing formalized representation by using description logic to obtain a cross-modal logic atlas; the reasoning verification layer is used for performing reasoning verification on the consistency of the code and the document according to the cross-modal logic graph; the result processing layer is used for judging a verification result according to the reasoning verification condition, starting a contradiction positioning mechanism when inconsistency is found, and carrying out contradiction positioning and report generation to obtain an interpretable report; according to the technical scheme provided by the invention, the defects that the consistency between the code and the document is difficult to accurately verify, and accurate contradiction positioning and an interpretable report are difficult to provide can be effectively overcome.

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

Access to log event data and corresponding source code is obtained and static code analysis is performed on the source code to produce analysis output. First vectors representing the log event data and second vectors representing the analysis output are generated. A similarity analysis is performed on the first vectors and the second vectors. A probabilistic relevance score associating a given log event with a segment of the source code is determined based on the similarity analysis. A visualization is generated for log events based on the probabilistic relevance score.

The invention provides an intelligent analysis method, system, equipment and program product for automatic code review, which are applied to the field of computer software engineering and development tools, and the method comprises the following steps: obtaining code difference data, preprocessing to generate a semantic code unit, constructing a structured cue word and inputting the structured cue word into a large language model, according to the scheme, the problems that in existing code review, semantic understanding is insufficient, and efficiency is low can be solved, automatic risk identification and analysis of code changes are achieved, and review efficiency and accuracy are improved.

The invention discloses a method for automatically capturing semantic information of a binary code block and a related device, and relates to the technical field of computers.The method comprises the following steps that code disassembling and instruction level analysis are conducted on the binary code block, and a binary stream original instruction sequence is generated; performing semantic intermediate representation conversion and vocabulary overflow standardization on the binary stream original instruction sequence to obtain a binary code standardized instruction sequence; obtaining an instruction data dependency relationship and an instruction control dependency relationship corresponding to the binary code block, and constructing a corresponding binary code program dependency graph; semantic code node embedding is carried out on the binary code program dependency graph to generate a binary code representation vector corresponding to context semantics; and performing automatic semantic capture reasoning on the binary code representation vector corresponding to the context semantics through a pre-trained semantic understanding model to obtain semantic information of the binary code block. According to the method, the semantic information of the binary code block can be efficiently and accurately captured.

Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

A codebase system that maintains and utilizes a dynamic mapping between a collection of tests and files within a codebase to ensure error detection and code validity. The codebase system accesses a collection of tests configured to process files within the codebase to identify errors. It then generates a mapping by executing these tests to determine the specific files each test operates on. Upon detecting updates to the test collection, the codebase system updates this mapping by re-executing the updated tests, identifying the new set of files for each test, and aggregating these updates into the existing mapping. When changes to the files in the codebase are detected, the codebase system accesses the updated mapping to identify which tests relate to the altered files. The codebase system then executes this relevant subset of tests to validate the modifications made to the files.

A code analyzer implements machine learning to detect vulnerabilities in computer code. The code analyzer trains a machine learning model using training vectors that characterize vulnerable programming patterns. The code analyzer evaluates a topological representation of the computer code using the machine learning model to identify a potential vulnerability. The potential vulnerability corresponds to a portion of the computer code where an attack can be used to bypass a security procedure. The code analyzer tests the potential vulnerability by emulating a fault injection during execution of the portion of the computer code. Upon confirming that the potential vulnerability can be exploited via a fault injection, the code analyzer generates a training vector that characterizes a vulnerable programming pattern associated with the portion of the computer code. The training vector can be used to further train the machine learning model.

In some implementations, a device may obtain an indication of a proposed change to a subset of executable code from a set of executable code. The device may determine, via one or more models, first context information associated with the proposed change and second context information associated with at least one of the subset of executable code, the set of executable code, or a context of the subset of executable code within the set of executable code. The device may determine a scrutiny level for review of the proposed change based on at least one of the first context information or the second context information. The device may obtain, via the one or more models, review information associated with the proposed change, wherein the one or more models apply the scrutiny level to obtain the review information. The device may perform an action based on the review information.

The invention discloses a knowledge extraction and envelope coverage-oriented software vulnerability test method and related equipment, and the method comprises the steps: obtaining vulnerability key information of target software, and obtaining sensitive function features through defect dependence analysis and construction based on the vulnerability key information; performing semantic analysis on the analysis report and the code abstract associated with the sensitive function characteristics, screening to obtain a target sensitive function, performing path envelope reverse tracking on the target sensitive function, and constructing to obtain a target path envelope; and obtaining coverage information enveloped by the target path, and carrying out fuzzy testing on the basis of the coverage information in combination with the iteratively optimized variation sample. According to the method, through intelligent closed loop of analysis-positioning-testing-feedback-optimization, static analysis provides accurate guidance for dynamic testing, and the static analysis strategy is inversely optimized by the result of the dynamic testing, so that the maximum improvement of the testing efficiency and the vulnerability discovery accuracy is realized in limited testing resources, and the testing efficiency and the vulnerability discovery accuracy are improved. The method can be widely applied to the technical field of software security.

The invention relates to the technical field of computers, in particular to a source code hierarchical positioning method, electronic equipment and a medium. Determining a target hierarchy corresponding to a target code line, a code range corresponding to the target hierarchy and code ranges of all target sub-hierarchies based on the code line hierarchical structure information of the code file; and displaying the code lines belonging to the target hierarchy, the code range corresponding to the target hierarchy and the code range of the target sub-hierarchy in the code lines in the visual range by adopting different display identifiers. According to the invention, code levels concerned by a user can be quickly and accurately positioned.

The invention provides a network security malicious code binary search method and system, and relates to the technical field of data processing, and the method comprises the steps: calculating the multi-dimensional structural similarity between a to-be-detected binary file and each standard binary reference file based on a function level control flow semantic feature vector; determining a finally matched standard binary reference file according to the multi-dimensional structural similarity; performing fine-grained difference analysis on the binary file to be detected and the finally matched standard binary reference file to obtain a fine-grained difference analysis result; and on the basis of a fine-grained difference analysis result, in combination with sensitive data operation behavior feature detection, judging whether the to-be-detected binary file is a maliciously tampered version, and positioning a malicious code injection point. According to the method, the defects that in the prior art, dependence on fixed features is too high, and compilation optimization is sensitive are overcome.

The invention belongs to the technical field of electrical digital data processing, and particularly relates to a Hash cache-based software component analysis optimization method, which comprises the following steps of: S1, scanning multi-dimensional features for the first time, and calculating storage component Hash by fusing dynamic behavior features, the method comprises the following steps: acquiring dynamic behavior characteristics of components during operation through a lightweight sandbox environment, and extracting basic characteristics, content characteristics and dependency characteristics of a software package and each component; hash values are introduced to serve as unique identifiers of software packages and components, and the Hash values of the components and corresponding detection results are calculated and stored during first-time scanning; according to the method, the component hash value is verified preferentially during subsequent scanning, if the hash value exists in the cache library, the historical detection result is directly reused, the full-amount analysis process does not need to be repeatedly executed, and on the premise that analysis accuracy is guaranteed, repeated calculation is reduced through a cache reuse mechanism, SCA analysis efficiency is improved, and resource consumption is reduced.

Techniques for natural language based chat are described. In some examples, a chat service receive a request to answer a natural language query (NLQ), generate one or more embedding vectors for the NLQ, performs a search of a vector database having information regarding one or more of enterprise context, natural language summaries of enterprise context, embedding vectors for the enterprise context, and embedding vectors for natural language summaries of the enterprise context according to the request to generate a search result, augmenting the NLQ using the search result, predicts a response for the NLQ using the search result augmented NLQ, and provides the predicted response in the chat.