45 results about "Program slicing" patented technology

The invention provides a rapid construction and reuse method and system for an application framework, and relates to the technical field of application framework construction, and the method comprises the following steps: mapping code snippets to a high-dimensional tensor space, extracting multi-level code features, and constructing a code association graph and a deep semantic network; constructing a hierarchical Bayesian network based on the spatial-temporal feature map, executing variational reasoning and adaptive particle filtering, and generating a reconstruction code; driving a multi-level generation network by using a dual-dimension discriminator, optimizing a code structure and a dependency relationship, and generating a final optimization code through program slicing and incremental synthesis; according to the method, rapid construction and reuse of the application framework are realized, and the code quality and the execution efficiency are improved.

The invention discloses a deep learning framework-oriented program slice defect positioning method, which comprises the following steps of: inputting an original failure test case causing defects, and extracting a target variation point; a variation rule oriented to a deep learning framework is designed; generating a mutation test case which is highly similar to the mutation execution path of the original failure test case and rejects an error statement; historical iteration data and the state of a current test case are comprehensively considered, the most suitable mutation operator is selected recursively, and code elements containing errors are gradually eliminated; executing the original failure test case and the mutation test case, and collecting coverage information in the execution process; and according to the collected coverage information and the passing / failure state of the suddenly changed test case, carrying out defect positioning by using a defect positioning algorithm based on a frequency spectrum, and outputting an accurate defect positioning result. According to the method, the defects in the codes in the program slices can be quickly locked and repaired, and the overall reliability of the deep learning framework is effectively improved.

The invention relates to a homology analysis method and device for binary code files and computer equipment. The method comprises the following steps: obtaining and identifying a function call graph corresponding to each binary code file through a security monitoring program; identifying isomorphic sub-graphs among the binary code files through a hybrid search strategy, and performing symbol program slicing processing on respective write function nodes in each isomorphic sub-graph to obtain symbol program slicing codes corresponding to the respective write function nodes of each isomorphic sub-graph; performing escape processing on each symbol program slice code corresponding to the isomorphic sub-graph to obtain each piece of mathematical semantic information corresponding to the isomorphic sub-graph, and performing similarity calculation to obtain a code similarity value between the binary code files; therefore, the homologous binary code file is identified. By adopting the method, the homology analysis accuracy of the highly confused binary code file can be improved.

The invention provides a computer network security software debugging method and system, and the method comprises the steps: setting a plurality of test nodes of a source program in target network security software, and positioning a key test node based on the execution state of each test node; constructing a target search domain when the source program is tested through the program context information of the key test node; determining a plurality of abnormal program slices in the source program based on the static control flow diagram and the dynamic control flow diagram of the program code in the target search domain; determining the test overhead of each program slice according to the test coverage information of each program slice and the path complexity when the source program executes the test data; determining the constraint level of each program slice according to all the test overhead and the membership relationship among the program slices; and debugging each program slice through the constraint level of each program slice. According to the scheme, on the basis of the constraint level of each program slice, the problem code line can be accurately pointed in the network security software debugging process.

The invention discloses a bug repairing method and system based on a grammar semantic tree, and the method comprises the steps: extracting a repairing mode fusing a grammar structure and a semantic intention from a historical bug repairing case, and guaranteeing the accuracy of the mode through program slicing guided by a key entity and iterative verification; on the basis of a double-constraint mechanism of grammar mergibility and semantic consistency, a specific mode is generalized to construct a hierarchical grammar semantic tree knowledge base; a knowledge base is used for generating a plurality of candidate patches for new vulnerabilities, single high-quality patches are generated through structure, grammar and semantic three-dimensional clustering and in-cluster integration and fusion, and the patches are output after heuristic sorting. According to the method, the defects that in the prior art, semantic understanding is shallow, and patch quality is unstable are overcome, and repairing accuracy, generalization ability and engineering practicability are remarkably improved.

The invention discloses a statement-level code vulnerability detection method based on fusion feature prompt tuning, which comprises the following steps of: firstly, analyzing a source code by utilizing a static analysis tool, and generating a slice code segment by utilizing a program slicing technology; then, combining the statement-level hard prompt with the statement-level soft prompt to form a fusion feature prompt; then, fusing the slice features and the statement features, and embedding the features into the model; and finally, realizing statement-level vulnerability detection by utilizing fusion feature prompt tuning and a pre-training model. According to the method, the prompt template is designed according to the characteristics of the vulnerability statements, pre-training knowledge can be fully utilized, and the structure and attribute information of vulnerability codes can be learned, so that the model can more accurately distinguish the vulnerability statements from non-vulnerability statements, and the possibility of false alarms is reduced. In addition, according to the method, the understanding ability of the pre-training model on the context can be fully utilized, and fine grammar and semantic information in codes can be captured.

The invention provides a slice test and form verification fused runtime anomaly detection method, which comprises the following steps of: extracting a minimum code snippet related to a potential abnormal program point from a to-be-detected program by adopting a program slice; generating a test case on the basis of the minimized code snippets related to the potential abnormal points; executing the minimum code snippets containing the test cases to obtain a detection result; according to the method, through an integrated form verification and program slicing technology, known anomalies can be detected, more potential anomalies, especially anomalies caused by complex input scenes and dynamic behaviors, can be covered through automatic path analysis, the abnormal path can be recognized in advance, and the method has the advantages that the method is simple and convenient to operate and high in practicability. And postmortem analysis after dependence on actual abnormity is reduced.

The invention provides a binary program variable type recovery method and device and a model training method and device. The recovery method comprises the steps of obtaining a target binary program; obtaining a plurality of to-be-recovered samples according to the target binary program; determining a target training sample with the highest similarity with the to-be-recovered sample in the training sample set through the target learning model; and determining the data type corresponding to the target training sample as the data type of the variable corresponding to the to-be-recovered sample. According to the method and the device, binary program slicing is carried out by using the data stream information between the functions to obtain the training sample containing the cross-function variable information, so that the training sample with richer information amount is obtained. The target learning model obtained after the first learning model is trained based on the training sample set can reliably recognize more important features from complex and uncommon data types, so that the data types of the variables can be recovered more accurately.

The application discloses a vulnerability type adaptive program slicing method and device and electronic equipment, the method filters program statements in a patch through a large language model, identifies program statements related to a vulnerability, and accurately determines a vulnerability-related slicing standard; the large language model is used to analyze the patch description and the program statements, the type of the vulnerability repaired by the patch is judged, and a suitable slicing strategy is constructed according to different vulnerability characteristics; the large language model is used to judge the integrity of the sliced code segment, so that the sliced path explosion is prevented, and the high integrity of the sliced code segment is ensured. The application can achieve the purpose of adaptively realizing high-integrity program slicing according to the type of the vulnerability.

The present invention relates to a method for inferring the type of a deep neural network based on program slicing. First, a large number of dynamic language program projects containing type annotation information are obtained, the type information in the projects is extracted, and a type information data set is constructed; then, the extracted type information data set is embedded in vector form by using encoding technology; finally, the embedded vector is used to train a deep neural network model, and the trained model is used to predict the type of program variables or function signatures. The purpose of the present invention is to solve the problems of low efficiency and low accuracy of type inference for dynamic language programs existing at present, improve the readability, understandability and maintainability of dynamic language programs in the production practice of software engineering, and ultimately achieve the goal of improving software quality assurance.

The invention relates to a simulation analysis method and device for program fragments. Comprises: constructing an original program file of a target program; running a target program fragment in the original program file through a simulator, capturing a corresponding running state, and generating check point data; merging the check point data into the original program file to obtain a to-be-tested program file; and according to the check point data in the to-be-tested program file, recovering the running state of the target program fragment in the to-be-tested program file in the test environment, and obtaining the performance data of the target program fragment during running of the target program. By optimizing storage and recovery processes of check points, the number of times of operation of a specified program on a simulator is reduced, tracking of a CPU register, codes and data is avoided, and the performance is improved; meanwhile, the construction method of the test instruction slicing system is improved, so that the generated program slices can run in target environments such as a simulator, a simulation environment and an FPGA without depending on a specific test system, and portability and test efficiency are improved.

The application provides a computer network security software debugging method and system. A plurality of test nodes of a source program in target network security software are set, a key test node is located based on an execution state at each test node, and a target search domain when the source program is tested is constructed by program context information of the key test node. A plurality of program slices with abnormalities in the source program are determined based on a static control flow graph and a dynamic control flow graph of program code in the target search domain. Test overhead of each program slice is determined according to test coverage information of each program slice and path complexity when the source program is tested. Constraint levels of each program slice are determined according to all test overhead and membership between each program slice. Each program slice is debugged through the constraint level of each program slice. The above scheme can accurately point to a problem code line in a network security software debugging process based on constraint levels of each program slice.

The invention relates to the technical field of code similarity detection, and discloses a code similarity dynamic detection method based on ensemble learning and program slicing, which comprises the following steps: generating an abstract syntax tree and an original code attribute graph of each source code; obtaining grammar, data and control information of each node, and constructing an extended code attribute graph of each source code; performing program slicing on the extended code attribute graph to generate program slicing sub-graphs, and standardizing node names of the program slicing sub-graphs through an automatic mapping method based on semantics; inputting a standard program slice sub-graph into a graph neural network to capture graph features, and inputting the abstract syntax tree into a Transform encoder to extract sequence features; and finally, fusing and integrating the two generated features and carrying out a similarity detection task. According to the method, high-precision code classification and efficient semantic analysis in a cross-language scene are realized.

The invention provides a smart contract code auditing method fusing program slices and RAG, and relates to the technical field of smart contracts. According to the method, a verified vulnerability contract is obtained through a vulnerability knowledge base construction stage, and a labeling data set covering n types of OWASP standard vulnerabilities is constructed to form a vulnerability knowledge base as an RAG database; in the online detection stage, firstly, a to-be-detected smart contract is dynamically sliced, and a three-dimensional feature vector of a code snippet of the to-be-detected smart contract is obtained; then adopting a hierarchical matching strategy, and using all features of the to-be-detected smart contract code snippets to perform retrieval recall in the RAG database; and finally, designing a chained reasoning framework to deeply fuse the matching result of the large language model LLM and code analysis, and giving the result to the LLM for reasoning to realize vulnerability detection. According to the method, a three-stage processing flow of retrieval, filtering and refinement is constructed in a detection stage, so that the detection precision and the calculation overhead are effectively balanced.

The invention discloses a Rust program slicing method and system based on a high-order function, and relates to the technical field of functional programming and program analysis. The method comprises the steps of program preprocessing; analyzing in the process; inter-process analysis and program slice calculation. According to the method, slice information in the process is obtained by utilizing high-order function characteristics, and the function abstract expressed in the high-order function form is generated, so that function parameters in the function abstract can be conveniently substituted at a calling point as required during inter-process calling, and a corresponding relation between the function parameters and actual parameters can be quickly obtained; compared with a traditional abstract-based analysis method, more information can be carried in the parameter transmission process, so that the inter-process slicing efficiency can be effectively improved, various hypergraphs do not need to be constructed, the problem of low analysis efficiency caused by huge graphs and complex composition process is avoided, and the analysis efficiency is improved. Therefore, the space overhead in the analysis process can be effectively reduced, and the inter-process program slicing efficiency is improved on the premise that the detection precision is not lost.

This invention discloses a statement-level code vulnerability detection method based on fused feature prompt tuning. First, static analysis tools are used to parse source code, and program slicing technology is used to generate sliced ​​code segments. Statement-level hard and soft prompts are then combined to form fused feature prompts. Slice features and statement features are then fused and embedded into a model. Finally, statement-level vulnerability detection is achieved using fused feature prompt tuning and a pre-trained model. This method designs prompt templates based on the characteristics of vulnerable statements, fully leveraging pre-trained knowledge to learn the structure and attribute information of vulnerable code. This allows the model to more accurately distinguish between vulnerable and non-vulnerable statements, reducing the likelihood of false positives. Furthermore, this method can fully leverage the pre-trained model's ability to understand context, capturing subtle grammatical and semantic information in the code.

The present invention provides a method and system for rapid construction and reuse of an application framework, which relates to the technical field of application framework construction, including mapping code snippets to a high-dimensional tensor space, extracting multi-level code features, and constructing a code association graph and a deep semantic network; constructing a hierarchical Bayesian network based on a spatiotemporal feature graph, performing variational reasoning and adaptive particle filtering, and generating reconstructed code; using a dual-dimensional discriminator to drive a multi-level generation network, optimizing code structure and dependency, and generating final optimized code through program slicing and incremental synthesis; the present invention realizes rapid construction and reuse of an application framework, and improves code quality and execution efficiency.

The invention discloses a smart contract vulnerability detection method based on a slice contract attribute graph, which comprises the following steps: firstly, obtaining a vulnerability detection sample set, respectively constructing a slice contract attribute graph for a smart contract in each vulnerability detection sample in the vulnerability detection sample set, and taking the slice contract attribute graph corresponding to each smart contract as input; the label corresponding to the smart contract serves as expected output, a training sample is formed, a vulnerability detection model is trained, when vulnerability detection needs to be conducted on a certain smart contract, a slice contract attribute graph of the smart contract is constructed, the slice contract attribute graph is input into the trained vulnerability detection model, and a detection result whether vulnerability exists or not is obtained. According to the method, a program slicing technology and a bidirectional sliding window are adopted to add dependent edges in a control flow graph and delete irrelevant nodes to generate a slice contract attribute graph, so that the accuracy and robustness of intelligent contract vulnerability detection are improved.

The application provides a vulnerability false alarm elimination method for a continuous integration platform, which captures vulnerability warning code features generated by program static analysis through a deep learning neural network, sorts and classifies the warnings, thereby eliminating false alarms in the warnings, and updates and optimizes the model using incremental warning data generated in the project continuous integration process. The steps include: obtaining historical warning data and corresponding warning label data of a Java project on a continuous integration platform; performing program slicing, tokenization and vectorization on the program corresponding to the warning; training a deep learning neural network to obtain a vulnerability false alarm elimination model; and adding the model to the continuous integration process of the project to enable the model to be continuously updated and optimized with project iterations. The application reduces the time cost of developers checking program static analysis vulnerability warnings in the project continuous integration process and improves the efficiency of processing warnings.

This invention belongs to the field of code security detection and automated repair technology. Specifically, it is a closed-loop system for automated repair and verification of code vulnerabilities based on unit tests. It completes vulnerability root cause localization and feature extraction through program slicing and static analysis. Then, based on the vulnerability root cause, it generates reproducible test cases with multi-dimensional assertions and regression test cases that meet coverage standards. Candidate patches are generated through retrieval enhancement and invalid patches are filtered out by static pre-verification. Patch verification is completed in an isolated sandbox using core and auxiliary dual-dimensional rules. Failure results are classified into scenarios and structured semantic feedback is generated to guide iterative repair. Finally, successful cases are structured and stored in a knowledge base in the form of triples to achieve self-evolution. This invention solves the core problems of existing technologies, such as unstable reproducible test cases, low iterative convergence efficiency, inability to identify overfitted patches, and lack of self-evolution capabilities. It can achieve fully automated repair and verification of code vulnerabilities, meeting the requirements for production environment application.

The invention relates to the technical field of security threat monitoring, in particular to a system security threat monitoring method and system. Comprising the following steps: S1, converting a source code project file and an application program into corresponding intermediate language files; s2, performing static analysis on the intermediate language file based on a static program slicing method of an improved system dependency graph (SDG); s3, carrying out visual statistics and display on an analysis result, and accurately identifying a line number in the source code; s4, generating a corresponding analysis report to display the influenced component and the security dynamic state thereof, and detecting detailed information and influence range of the vulnerability; existing static program analysis is improved, whether codes conform to related regulation and national standard coding specifications or not can be found, by improving a static program slicing method based on a system dependency graph SDG, time complexity is remarkably reduced, universality is enhanced by adopting an intermediate language file so as to adapt to various programming languages, and the method has the advantages of being high in practicability and easy to popularize. And wider support is provided for static analysis.

The invention discloses a program slice generation method and device and electronic equipment, and relates to the technical field of load modeling. And the electronic equipment only compensates the memory access instruction which affects the execution correctness of the instruction stream data, so that the data stored in the memory access address with data abrupt change is recovered. Due to the fact that the electronic equipment does not need to compensate any instruction, the compensation instruction and the corresponding jump instruction of the instruction stream in the program slice generated by the electronic equipment are reduced, the execution frequency of the compensation instruction is reduced, and on the basis that the execution correctness of the instruction stream is guaranteed, the difference between the program slice and the original application program is reduced.

Embodiments of the present application provide a chip program debugging method and system without passive code and a medium. The method comprises: obtaining a chip program, slicing the chip program based on a program slicing technology to obtain a plurality of program slices; receiving a debugging instruction, analyzing function information of the debugging instruction based on the debugging instruction, and executing a corresponding debugging program according to the function information of the debugging instruction; linking the plurality of program slices to generate a plurality of debugging groups, performing program debugging on the plurality of debugging groups based on the debugging program, and generating a sub-debugging result corresponding to each debugging group; integrating the plurality of sub-debugging results to obtain a final debugging result, and saving the final debugging result to a register. The chip program is sliced by the program slicing technology, the plurality of program slices can be grouped and debugged or debugged in parallel, the debugging flexibility is improved, the number of program instructions analyzed by a developer is reduced, and the work efficiency is improved.

The embodiment of the invention provides an application program slicing processing method, terminal equipment and related products, and the application program slicing processing method comprises the following steps: in response to a slice establishment request initiated by an application program, determining whether the slice establishment request meets a preset slice establishment condition; when it is determined that the slice establishment request meets the preset slice establishment condition, the slice establishment request is sent to the network side device, and a response message returned by the network side device is obtained; judging whether the slice parameter information contained in the response message is matched with the slice requirement of the application program or not, and determining whether to trigger the network side equipment to execute slice establishment operation for the slice establishment request or not based on a judgment result. Through the embodiment of the invention, the risk of abnormal collapse of a chip and an operating system or network signaling storm caused by blindly initiating the session establishment request to the network by the terminal equipment can be avoided, the success rate of slice establishment is improved, and the application program obtains expected network performance and service quality.

The present application belongs to the technical field of software vulnerability mining, and specifically relates to a kind of Android application user privacy leakage vulnerability identification method.The present application includes: two-way program slicing analysis, privacy information identification based on correlation analysis and machine learning method assistance, and non-injective data transformation analysis.Two-way program slicing analysis is used to detect data received by Android application from the server end and judge whether they flow to user interface function;Privacy information identification based on correlation analysis and machine learning method assistance can comprehensively analyze which of the above server end returned information belongs to the privacy information of other users;Non-injective data transformation analysis is used to detect how user data is transformed on the client side before being presented to the user, and then judge whether the "data minimization" principle is followed and whether there is a privacy risk.

The program slicing method comprises the steps that a PC address of at least one target instruction is obtained, the PC address of the at least one target instruction is cached in a cache unit, in the process of executing a target program, a tracking module collects an accessed memory address and accessed data when the PC address of the at least one target instruction cached in the cache unit is executed through the PT / CoreSight technology, and the accessed memory address and the accessed data are stored in the cache unit. Obtaining a memory address and access information of at least one target instruction; and based on the at least one target instruction, the memory address of the at least one target instruction and the access information, slicing the target program to obtain a plurality of slices. According to the method, the memory address and the access information in the simulation process of the simulator are collected in a lightweight mode, the simulation process is prevented from being affected, and the bottleneck problem that a tracking module collects memory information corresponding to the PC address of any instruction indifferently, and consequently DDR bandwidth and disk falling occupation are large is solved.

The present application relates to the field of chip technology, and specifically discloses a K value selection method and related equipment for the K-means clustering algorithm in chip simulation. The method includes: obtaining a first number of first program slices of a benchmark test program corresponding to a target chip, calculating a first type of performance indicator data for the first program slice; performing feature analysis on the first type of performance indicator data to obtain a corresponding feature classification result; performing calculations based on the feature classification result to obtain a corresponding maximum preset K value, and generating a K value range based on the maximum preset K value; sending the K value range to a preset K value selection model, receiving a target K value output by the K value selection model, and finally using the target K value in the K-means clustering algorithm in chip simulation to obtain a corresponding simulation result, which not only reduces the chip simulation time, but also ensures the accuracy of the chip simulation result.

The present invention proposes a method and system for detecting vulnerabilities in Ethereum smart contracts based on a pre-trained model. In order to obtain more effective vulnerability semantic or syntactic information, the present invention is intended to be based on two different forms of code representation. Firstly, by analyzing information such as the data flow and control flow of the smart contract program, the program slicing technique is used to automatically extract the vulnerability code slices. Secondly, by analyzing the AST of the contract, the smart contract is compiled using a syntax parser, and the AST structured information is automatically obtained using a custom traversal method. At the same time, in order to achieve better detection effects, the present invention uses the pre-trained CodeBert model to fuse the two types of information, realize the fusion of features, and finally realize the detection of vulnerabilities with serious hazard levels in the current Ethereum smart contract environment. The present invention is targeted at smart contract developers, realizes the automatic detection of Ethereum smart contract vulnerabilities based on code fusion representation, and achieves the purpose of contract security guarantee.

This disclosure relates generally to the field of program slicing, and, more particularly, to a method and system for function call and variable access based relaxed backward slicing. The method discloses a slicing criterion which focuses only on functions called from an entry function. The slicing criteria uses control and data flow information to slice the given entry function with respect to functions called from the body of the entry function and eventually remove all functions not called directly or indirectly from the entry function. The variables modified by calls in the entry function are considered through side-effect while identifying control and data dependence chain within body of entry function. The proposed technique identifies partitions of functions based on the variables accessible in and functions called from the entry function. Thus, unrelated sets of functions with respect to the entry function are computed and divided into different partitions.

The invention provides a vulnerability assessment optimization method based on code simplification, and belongs to the technical field of software engineering. Comprising the following steps: (1) a code preprocessing stage: performing program slicing on a source code to ensure the semantic integrity of a code snippet as much as possible; (2) an attention weight generation stage: pre-training a Transform model by using a code; (3) a code simplification stage: according to a defined code simplification strategy, simplifying each group of code snippets processed in the stage (1) to obtain simplified codes; and (4) a model fine tuning stage: performing fine tuning on the code pre-training Transform model according to the simplified code in the stage (3), and training a vulnerability assessment model. The method has the beneficial effects that on the premise that the model evaluation accuracy is ensured, the construction and reasoning expenses of the model are reduced; and meanwhile, the severity of the vulnerability is evaluated in time in a resource limited scene.