270 results about "Control flow" patented technology

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention relates to an automatic code auditing method and device, computer equipment and a storage medium. The automatic code auditing method comprises the steps of obtaining a grammar structure, a control flow and a data flow of a to-be-audited code; constructing a context graph of the to-be-audited code according to the grammatical structure, the control flow and the data flow of the to-be-audited code; obtaining a multi-modal collaborative vulnerability detection method, wherein the multi-modal collaborative vulnerability detection method comprises a static analysis method based on rule matching, a symbolic execution method based on a code path, a large model reasoning method based on semantic understanding and weights of the methods; and identifying one or more code vulnerabilities, the vulnerability type of each code vulnerability and the confidence coefficient according to the context graph of the to-be-audited code and the multi-modal collaborative vulnerability detection method. According to the method, the audit codes of various vulnerability types can be processed while the code audit efficiency can be improved.

The invention provides a syntactic structure tree-based code change influence intelligent analysis method and system, and the method comprises the steps: extracting a control flow, a data flow and a call flow according to an AST generated through the analysis of an original code and a changed code, and constructing a static dependency graph SDG containing a multi-dimensional dependency relationship; the method comprises the steps that a base layer is used for comparing AST differences before and after code change and generating grammatical structure change feature vectors; the service layer matches a corresponding service label for the changed AST node by using the service domain template; the change labeling layer constructs a cross-version dependency chain for the change AST nodes and generates historical feature vectors; and calculating a risk value of changing the AST node by utilizing a feature calculation result and combining with a multi-dimensional influence diffusion algorithm, and automatically generating a decision suggestion. According to the method, multiple dimensions of a control flow, a data flow and a call flow are deepened, risk prediction is optimized by utilizing natural language processing and historical change data, and the influence brought by code change is assisted to be better understood and managed.

The invention relates to an automatic program repairing method based on data flow driving, which takes an error file and a method signature as input, and reports error information in a defect positioning tool or an integrated development environment. Secondly, handing over error information and original defect codes to a large language model, extracting an element list related to defects from the large language model, then constructing defect contexts, using a search tool to retrieve key element definitions and dependency relationships, integrating control flow and error mode information, and finally obtaining defect information; then, the retrieved information, the original defect codes and the potential defect types are integrated into cue words, and the cue words are submitted to a large language model to generate candidate patches. And then the candidate patch is verified, and if the candidate patch does not pass the verification, error reporting information during the verification is combined with the candidate patch to carry out iterative optimization until the candidate patch successfully passes all test verification or the maximum number of iterations is reached. According to the method, context semantics are enhanced by means of data flow analysis, and the repairing accuracy and efficiency are improved.

The invention discloses a test generation and defect prediction method and system based on a heterogeneous program diagram, and the method comprises the steps: constructing a heterogeneous dynamic program dependency diagram: extracting an abstract syntax tree, a control flow diagram and a program dependency diagram of a source code through static analysis, capturing an execution track during operation in combination with dynamic instrumentation, and integrating version evolution information, forming a heterogeneous dynamic program dependency graph containing various types of nodes and edges; heterogeneous graph neural network modeling: designing a four-layer eight-header heterogeneous graph neural network model based on the heterogeneous dynamic program dependency graph, and outputting a function level defect probability and a test case sequence by adopting a type awareness attention mechanism and time coding injection; and multi-task joint training and reasoning: adopting an end-to-end training strategy, jointly optimizing defect prediction loss and test generation loss, preferentially screening high-risk functions during reasoning, and generating a coverage test case. According to the method, the defect detection accuracy is remarkably improved, the test coverage rate is increased, and the method is suitable for enterprise-level complex software systems.

The invention discloses a compilation optimization pass combination optimization method based on deep reinforcement learning. The method comprises the following steps: 1) collecting a reference program data set for various general function tests; 2) constructing a deep reinforcement learning-based compilation optimization pass-combination optimization strategy model for a compilation optimization pass-combination optimization process, wherein the compilation optimization pass-combination optimization strategy model comprises agent D3QN construction and definition of a reward space, a state space and an action space; 3) introducing a composite program feature combined with basic features such as a control flow embedding vector extracted by the graph neural network, an instruction number, a basic block number and the like into the expression of the state space, enriching the expression ability of the state, optimizing the pass based on the state selection and acting on the intermediate expression by the D3QN, generating an award, and obtaining the state space; and repeating the loop of feature extraction-action selection-reward generation until the maximum action upper limit is reached, and generating an optimized pass combination adapted to the program features. The problem that in existing compiling optimization, the compiling optimization effect on part of programs is poor is solved.

The invention discloses a computer interactive programming platform automatic evaluation system based on deep learning, and particularly relates to the technical field of deep learning, the system comprises five modules, namely a structure diagram construction module, a semantic diagram generation module, a dynamic evaluation trigger module, a semantic solution identification module and an exponential deduction feedback module, and full-process evaluation from code structure abstraction to semantic modeling is realized. The system dynamically controls a deep evaluation process through a structure matching rate and a logic deviation degree, identifies a potential innovation solution, generates a semantic equivalence degree and a path innovation index, outputs a grade in combination with a neural network score, and embeds a high-innovation sample into a training buffer area to update a model, so as to realize intelligent evaluation and adaptive optimization; according to the method, the modeling precision of a program structure and a control flow is improved, dynamic regulation and control of semantic understanding and an evaluation process are realized, a potential innovation solution can be identified, and model self-updating is realized through scoring grade feedback, so that the intelligent analysis and adaptive evolution capability of an evaluation system on complex programming behaviors is enhanced.

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining a learned control flow directed graph for executable code of an application by observing executions of transitions during an observation period and determining destinations of indirect transfers based on the learned control flow directed graph. Next a disassembly of the executable code is determined based on the learned control flow directed graph, the destinations of the transfers, and the executable code.

The invention discloses a mimicry thought-based code dynamic obfuscation compiling method, system and equipment. The method comprises the following steps of: sensing a source code, and performing compiler annotation on a key function in combination with a large language model and expert knowledge; a global character string constant is extracted, an ASCII value of a character is coded into an MBA expression, a decoding function is inserted into a position where the constant is used, mimicry replacement is performed on an original character string in a data segment, and functions of other programs are disguised; adding a clone block irrelevant to function logic, and constructing a mimicry branch by using a dynamic opaque predicate as a jump condition; adding an equivalent heterogeneous shadow branch, and using a random predicate as a jump condition; a flower instruction is inserted, and an MBA expression is used for instruction replacement; and performing control flow flattening on the function. According to the method, the mimicry multi-variant thought is combined, accurate diversified confusion compilation is performed, and key fragments of program codes in the fields of industrial control, power grids, Internet of Things and the like are effectively protected.

The invention discloses an instruction-level code optimization method and device based on template matching, a medium and equipment. According to the method, instruction-level codes serve as input, the input instruction-level codes are firstly constructed into corresponding control flow diagrams, then value ranges of active variables and register quantities are initialized based on the control flow diagrams, then variable assignment instructions and related instructions are found out from instruction sequences of basic blocks to form instruction segments to be optimized, and the instruction segments to be optimized are optimized. The method comprises the following steps of: optimizing an instruction fragment, obtaining an optimized instruction fragment through template matching, performing SMT equivalence verification on the instruction fragment before and after optimization based on an active variable and a value range of a register quantity, and finally optimizing an input instruction-level code in a manner of replacing the instruction fragment before optimization with the optimized instruction fragment which is verified to be equivalent. And finally, the optimized instruction-level code is obtained.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention discloses a cross-modal adaptive fine tuning method and system based on a double-branch network architecture, and realizes cross-modal knowledge fusion and migration by combining the modeling capability of a graph neural network on code graph structures such as an abstract syntax tree, a control flow graph and the like and the powerful semantic understanding and generation capability of a large language model. A double-branch architecture is adopted, a graph structure and text input are coded respectively, and feature interaction is achieved through heterogeneous adaptation, structural decoupling and a mask attention mechanism. In a fine tuning stage, three-stage strategies are provided: basic fine tuning of structure perception, efficient parameter migration by combining Adapter, Prefix Tuning and LoRA, and language generation quality and robustness are optimized based on an unsupervised text reconstruction task. The training stage relates to structure sensing tasks such as node prediction, edge reconstruction and subgraph comparison, and the stability and generalization ability are improved in combination with course learning. The method is widely applicable to tasks such as code completion, question and answer and vulnerability detection, and has the advantages of strong structure perception, efficient cross-modal fusion, low training cost and the like.

The invention discloses an automatic code defect repairing method based on a code attribute graph and a large model, which comprises the following steps: collecting a code sample, analyzing a source code, generating the code attribute graph fusing an abstract syntax tree, a control flow graph and a data flow graph, and then carrying out dynamic pruning according to node importance calculated based on node degrees, PageRank and data propagation dependency, so as to obtain a large model; key nodes are reserved to improve the graph learning efficiency; extracting features of the optimized graph through GAT, introducing multi-scale coding, and generating a graph embedding vector; in combination with node importance, high-value Tokens are screened, semantic embedding of structure perception is carried out, and more accurate text representation is obtained; generating a comprehensive feature by fusing the image and the text vector, and then inserting a mask at a position with weak dependence or high risk; performing fine tuning optimization on the mask position; and inputting the comprehensive features and the mask source codes into a pre-training language model to generate patches, performing multi-task evaluation and reordering, and selecting an optimal patch to complete repair.

The invention relates to the technical field of block chains, in particular to a smart contract vulnerability detection method and system based on a multi-modal large language model.The detection method comprises the steps that S1, the system receives source code input of a smart contract; s2, executing semantic branches and graph structure branches in parallel; executing semantic branch processing, calling an annotation agent, and generating an annotation from the source code of the smart contract; calling a vectorization agent, coding the annotation and the source code, and converting the annotation and the source code into a high-dimensional vector; executing graph structure branch processing, compiling a source code into a byte code by the system, and generating a control flow graph; the vectorization agent generates high-dimensional embedded representation of the nodes based on the control flow graph; and S3, integrating features extracted from the semantic branches and the graph structure branches through a multi-modal feature fusion strategy, and inputting the integrated features into a classifier to detect vulnerabilities. The method is based on a multi-modal large language model agent, and potential vulnerabilities in the smart contract are comprehensively analyzed by integrating high-level semantic information and low-level structured data.

The invention relates to the technical field of software security, and discloses a variational inference-based micropatch repair path adaptive optimization method, system, device and medium, and the method comprises the steps: constructing a joint control flow diagram and a data flow diagram of a to-be-analyzed program, and forming a program structure diagram; based on the program structure diagram, modeling and sampling potential variables of the repair paths through variational inference, and generating a plurality of candidate repair paths; for each candidate repair path, generating a micropatch statement according to context semantics of each node in the path, and inserting the micropatch statement into a corresponding instrumentation position in the source code; and performing multi-dimensional scoring and sorting on the candidate repair paths, and selecting an optimal repair path for deployment according to a scoring result. According to the method, the structural breakthrough of the whole process is realized in the aspects of path identification, patch generation, strategy optimization, engineering deployment and the like, and a technical support is provided for constructing a software vulnerability repair system with autonomous generation, semantic understanding and controllable deployment capabilities.

The invention belongs to the field of industrial control safety, provides a PLC (programmable logic controller) memory behavior analysis-based control system configuration logic anomaly detection method, and solves the problems of logic attack detection blind areas, incomplete detection coverage, process semantic deficiency and the like caused by hidden attacks (modifying PLC control logic through a legal programming interface). The method comprises the following steps: S1, collecting a PLC register with a timestamp and a coil state sequence; s2, identifying a key memory address, constructing an address cluster set, and supporting event completion and process mining; s3, generating an XES standard event log by adopting a clustering and symbol back-pushing method; s4, improving a Heuristics Miner algorithm, automatically identifying a starting point and an end point of a control flow, and constructing a dependency graph with an identifier; s5, converting the control logic into a formalized model, and defining a legal state transition sequence; and S6, monitoring a real-time event stream on line, mapping the real-time event stream to the model and dynamically playing back the real-time event stream, and realizing anomaly detection by combining track alignment and comparison. The method is complete in coverage, clear in semantics, capable of tracing attack paths and capable of improving the defense capability of a logic layer.

The invention discloses an intelligent contract vulnerability detection method and system based on semantic comprehension and program path analysis, and belongs to the technical field of network security. The method comprises the following steps: firstly, analyzing an intelligent contract code by using a large language model, and reasoning to generate a structured security rule for defining a taint source, a taint sink and a purifier; secondly, guiding a taint analysis engine by using a security rule, and tracking on a data flow and a control flow diagram of a program so as to efficiently screen out a high-risk taint path; then, carrying out reachability verification on the high-risk path by adopting a symbolic execution technology; and finally, performing final context review on the verified vulnerability path by using the large language model again to generate a vulnerability report. According to the method, the semantic comprehension ability of the large language model and the preciseness of traditional program analysis are subjected to multi-stage cooperation, so that the detection precision and efficiency of the intelligent contract logic vulnerability can be remarkably improved, the interpretability of a report result is greatly enhanced, and the method has important application value.

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.

The invention discloses a static binary code taint analysis method based on a propagation action scope, and relates to the field of static binary code taint analysis, and the static binary code taint analysis method comprises the following steps: extracting a program instruction set and a control flow structure based on a disassembling result of a target binary code; calculating a value set with a source of the binary code based on the control flow graph; on the basis of the control flow diagram and the value set with the source, executing cross-function stain propagation analysis, and identifying memory positions or registers influenced by pollution in each function and propagation action ranges of the memory positions or the registers; and extracting all instruction sets using the taint data based on the taint and the propagation action range thereof. According to the method, the false alarm rate of static binary taint analysis can be reduced and the instruction set involved in the taint analysis can be reduced without increasing the analysis overhead, so that the method has important significance in improving the instrumentation efficiency and the operation efficiency of dynamic taint analysis and improving the accuracy of protocol reversion, fuzzy testing and vulnerability mining based on the taint analysis.

The invention discloses a compiling method for compiling a C language source code into an RISC-V assembly code. The method comprises the following steps: acquiring a C language source code; performing lexical analysis on the C language source code to generate a mark flow; performing syntactic analysis on the mark flow, and constructing an abstract syntax tree; performing semantic analysis on the abstract syntax tree to generate a target abstract syntax tree; constructing a runtime environment of the RISC-V assembly code; generating an intermediate code and a control flow diagram corresponding to the intermediate code according to a rule in the runtime environment and the target abstract syntax tree; generating a target code by using the intermediate code and the control flow diagram corresponding to the intermediate code; according to the technical scheme, the intermediate representation more adaptive to RISC-V custom instruction mapping can be generated, so that the execution efficiency of assembly codes is improved; and meanwhile, by designing a lightweight runtime environment, the performance overhead is further reduced.

The invention relates to the field of code vulnerability detection, in particular to a binary vulnerability retrieval and positioning method and system based on high-dimensional vulnerability characterization, and the method comprises the steps: firstly inputting a binary file, analyzing the structural information of the binary file, and analyzing a machine instruction; extracting binary file function information, generating a cross-platform basic block IR instruction sequence, constructing a control flow graph, and constructing a data dependency graph; splicing the program dependency graph feature vector and the basic block IR instruction sequence feature vector, and performing multi-modal feature fusion to generate a comprehensive high-dimensional vulnerability representation vector; and finally, inputting the high-dimensional vulnerability representation vector into an index for searching to obtain a plurality of nearest neighbor indexes and distances, converting the distances into similarity, filtering according to a similarity threshold to obtain a most similar vulnerability vector, and obtaining vulnerability information and vulnerability positions corresponding to the vulnerability vector. The problems that traditional binary vulnerability detection cross-platform and compiler optimization detection fails and the result reliability is low are solved.

The invention relates to the technical field of software security, and discloses an IDA microcode-based digital multimeter customized code obfuscator construction method and system, and the method comprises the steps: carrying out the obfuscation replacement of a measurement algorithm and a data processing method in a digital multimeter code based on an instruction replacement technology; confusing a control flow of the digital multimeter based on a false control flow technology, namely obtaining a calling relation of basic blocks to reconstruct an original program, and avoiding generation of an endless loop in a symbolic execution process through a method of assigning a value to an opaque predicate in advance; randomly modifying variable names, identifiers and function names of the sensitive data information of the digital multimeter based on a variable name confusion algorithm; and carrying out decompilation and code conversion on the obfuscation algorithm based on the IDA microcode, and constructing the digital multimeter customized code obfuscator based on the IDA microcode. The method can be integrated into the development environment of the digital multimeter, the safety protection level of the digital multimeter is improved, and the measurement data is prevented from being illegally stolen.

The invention belongs to the technical field of power supply control, and particularly discloses a digital power supply graphical programming method based on flow driving, which comprises the following steps of: constructing a power supply control Flow component library which comprises a plurality of Flow nodes, and storing the nodes in a script file form, the script file comprises basic code snippets required by node function implementation and describes data terminals, logic terminals and node attribute parameters of input and output reserved by nodes; constructing a graphical programming module, and providing a graphical programming interface to receive a setting operation of a user so as to form a graphical program logic description file; generating a graphical program script file according to the description file and each Flow node script file; and generating a power supply control code based on the graphical program script file and the chip peripheral drive script file, and compiling and burning the power supply control code into an embedded controller of the digital power supply. According to the method, low-code / code-free development can be realized, and the development difficulty is effectively reduced.

The invention discloses a code conversion method and a related device, and the method comprises the steps: obtaining an input code, carrying out the grammatical analysis of the input code, and generating an abstract syntax tree which comprises a plurality of nodes; coding a code text in the input code to generate a first semantic vector; encoding the structure in the abstract syntax tree to generate a second semantic vector; processing the first semantic vector, and converting the first semantic vector into a second semantic vector; generating a control flow diagram according to the abstract syntax tree, and constructing a data flow diagram; determining a plurality of semantic units according to the control flow diagram and the data flow diagram; obtaining a preset domain knowledge graph, wherein the preset domain knowledge graph comprises a plurality of concept nodes; according to the concept nodes and the semantic units, analyzing the second semantic vector to obtain a target semantic vector, decoding the target semantic vector into a target language, and generating a target code. By adopting the method and the device, code conversion expansibility and accuracy can be improved.

The invention discloses a cross-platform binary code block hierarchical semantic similarity detection method and a related device, and belongs to the field of code vulnerability detection.The cross-platform binary code block hierarchical semantic similarity detection method comprises the following steps that structural information of a binary file is analyzed, a machine instruction is analyzed through a hybrid disassembling engine, a basic block is extracted from the structural information of the binary file, and the basic block is extracted from the binary file; constructing a control flow graph, a data dependency graph and a function call graph; extracting instruction level semantics, basic block level semantics and function level semantics from the instruction sequence, the basic block, the control flow graph, the data dependency graph and the function call graph; according to the instruction-level semantics, the basic-block-level semantics and the function-level semantics, constructing instruction-level features, basic-block-level features and function-level features; and carrying out hierarchical similarity detection on the instruction-level features, the basic block-level features and the function-level features. The method and the related device can solve the problems of low detection efficiency and poor accuracy when a traditional vulnerability detection method is used for detecting vulnerabilities.

The invention discloses a malicious code control flow feature extraction method based on a graph neural network. The method comprises the following steps: constructing a control flow graph, a data flow graph and a function call graph; designing a drawing neural network architecture; training a graph-level classifier; performing graph interpretation by using a GNNExplainer algorithm, attention mechanism analysis and a gradient analysis method; converting the extracted control flow mode into a structured detection signature, and mapping the structured detection signature to an original binary code; and integrating with a static analysis tool through a standardized interface. The invention further discloses a malicious code control flow feature extraction system based on the graph neural network. Multi-level graph structure representation is constructed, important information such as a control flow structure and a data dependency relationship is fully reserved, the deep structure similarity of malicious codes can be recognized, the deformation resistance is higher, and therefore the malicious code detection precision is improved; according to the method, key sub-graphs can be recognized, graph structure features are converted into detection rules, then the detection rules are integrated with existing static analysis tools, and practicability is improved.

The invention relates to the field of code vulnerability detection, in particular to a vulnerability detection method and system based on code block features. The method comprises the steps that firstly, a binary file is analyzed, and a machine instruction is analyzed through a hybrid disassembling engine; secondly, performing static linear scanning on the extracted binary file instruction, constructing a control flow graph, dividing basic blocks, extracting an operation code sequence in the basic blocks, and constructing a cross-basic-block data dependency graph and a system calling sequence called by the basic blocks; secondly, using an LSH algorithm to realize rapid filtering, performing similarity comparison on extracted basic block features and vulnerability feature library features, realizing coarse-grained matching, and screening out a matching candidate set; feature fusion is carried out on the candidate set basic block operation code sequence, the basic block data dependency graph and the system calling sequence called by the basic block, GNN fine-grained matching is carried out on the fused features, and vulnerability detection is carried out. The problems that traditional binary vulnerability detection is poor in adaptability and low in result reliability are solved.

The invention discloses a cross-language interaction malicious behavior detection method based on a high-order function, and relates to the technical field of computer security. The method comprises the following steps: receiving a source code of a certain program, modeling a cross-language interface in the program, and identifying and marking a cross-language calling boundary; different language source codes are converted into universal intermediate representations (IR), the IR is combined, and cross-language calling points are marked; constructing a global cross-language function call graph and a control flow graph, determining a function analysis sequence by inverse topological sorting to perform dependency analysis on each function, and abstracting dependency information in the processes into a function abstract in a high-order function form; and parameter instantiation is carried out when function calling is processed, so that the dependency relationship between the cross-language APIs is obtained. According to the method, efficient analysis and transmission of the cross-language dependency relationship are realized through the high-order function abstract and the dependency matrix, the high overhead of constructing a complex system dependency graph by a traditional method is avoided, and the analysis efficiency and the detection accuracy are improved.

Modifications to existing computer hardware, compiler changes or source-to-source transforms performed during the software build process, and a collection of libraries and modifications to existing standard system software and libraries. The invention allows a program author to enforce various kinds of locality of causality in software to provide enforcement of boundaries for the following aspects of a computer program: control, space, time, modularity, reference, initialization, and mutability. Where these properties do not suffice to guarantee a property at static time, dynamic checks may be added and the constraints on control flow prevent such dynamic checks from being avoided by the program.