43 results about "Program structure" patented technology

Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.

The invention provides a storage type XSS attack detection method and device based on a large language model, and the method comprises the steps: receiving an initial cue word, inputting the initial cue word into a cue word expander, carrying out the extension of the initial cue word, carrying out the performance evaluation to screen out a cue word meeting a preset performance index, carrying out the multi-round extension and evaluation, and obtaining a detection result. Until the optimized cue word is obtained; the method comprises the following steps: receiving a to-be-detected code, generating a program dependency graph of the to-be-detected code, decomposing the program dependency graph of the to-be-detected code into a plurality of sub-graphs with consistent program structures to obtain a plurality of sub-program dependency graphs, traversing nodes in each sub-program dependency graph, and reserving an original structure of a program; the nodes are sequentially converted into an original code format to obtain code representation fragments, the code representation fragments and the optimized cue words are input into a large language model to judge whether each code fragment is an attack vector of a storage type XSS or not, and a final speculation result is obtained. The method has the advantages of low detection cost, high accuracy and high efficiency.

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.

The invention discloses an intelligent graphic programming language application system and method based on a large model technology, and the system comprises an input module which is used for obtaining a programming logic document; the large model analysis and generation module is used for performing semantic analysis on the programming logic document and generating corresponding graphical programming language initial code representation; the graphical language generation module is used for converting the initial code representation into a program structure of a graphical programming language; the small model proofreading and optimizing module is used for verifying a program structure according to a preset graphic language grammar rule and automatically modifying and optimizing when a part which does not conform to the grammar rule is detected; and the output module is used for outputting the verified and optimized graphical programming language program. According to the intelligent graphic programming language application system and method based on the large model technology, the advantages of an artificial intelligence large model and an artificial intelligence small model are comprehensively utilized, and automatic conversion and optimization verification from a text requirement to a graphic program are achieved.

Systems and methods for efficiently converting program code from a source language to a target language. Input source code is parsed into an intermediate representation (IR) using a processor device. Structural and semantic models of the source code are established by applying static analysis to the IR, and a program architecture of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a single static assignment (SSA) form, and a system dependency graph (SDG) is constructed from the SSA form. The SDG is traversed to sort the translation tasks, and the sorted tasks are translated into a target language using a large language model (LLM). A translated program is generated by integrating the translated code segment into a coherent program structure in the target language.

The invention discloses a static vulnerability detection method and system based on a unified cross-language program structure diagram, and the method comprises the following steps: constructing the unified cross-language program structure diagram according to a to-be-detected source code and compiling metadata thereof, and building an interface semantic mapping relation on a cross-language interface edge; a cross-language path from a program entry to a sensitive operation point is extracted on the unified cross-language program structure chart, uncertain elements in the cross-language path are labeled, and the confidence coefficient of the path is calculated; performing semantic alignment on heterogeneous language states on a cross-language path based on a cross-language abstract domain, and converting a path condition and a vulnerability establishment condition into logic constraints; and carrying out satisfiability judgment on the logic constraint, outputting a vulnerability detection result according to a judgment result, and generating a structured evidence chain. According to the method, the cross-language evidence chain can be output, vulnerability paths, witness information of trigger conditions and cross-language conversion details can be clearly displayed, and work such as manual recheck and vulnerability repair is powerfully supported.

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

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

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

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 relates to the technical field of software testing, and discloses an LLM and SMT solver-based test case generation method, which is characterized in that a search type test is taken as a core drive, when coverage stagnation occurs, an execution path is subjected to semantic analysis by means of an LLM, a logic expression for describing a target path condition is generated, and in consideration of the limitation of the LLM in logical reasoning, a test case is generated; the system further introduces an SMT solver to perform verification and reasoning on a logic expression output by the LLM, finally feasible test input is generated, and the test efficiency and the coverage rate are improved. According to the method, the efficiency and precision of path coverage are improved, good adaptability and universality are achieved, the method is suitable for various languages and complex program structures, and the intelligent level and engineering practical value of automatic testing are effectively improved. The problem of insufficient accuracy of a large language model in complex path constraint reasoning is effectively relieved.

The invention provides an API sequence recommendation method based on user intention perception. The method comprises the steps of firstly constructing a training sample based on multi-source information; then carrying out joint modeling on the natural language description and the program structure representation by utilizing a pre-training language model to obtain unified representation fusing user intention and code semantics; secondly, predicting a candidate API sequence in an autoregression mode through a sequence generation model; secondly, in the reasoning stage, semantic enhancement is carried out on the input natural language query by adopting a query expansion technology so as to generate an expansion query; and finally, jointly inputting the extended query and the current code context into the sequence generation model, and outputting an API calling sequence conforming to the user intention. Natural language query and code context information are comprehensively utilized, the development intention is obtained from the natural language description input by the user, modeling is conducted on the programming environment in combination with the current code context, and the API recommendation result better meeting the actual development requirement is generated.

A platform that generates submittal reviews electronically for various construction projects. Embodiments include receiving project submittals by the reviewer, inputting the submittal in a web application interface, receiving the output in a specific format, reviewing the submittal review generated then downloading the reviewed submittal and handing it to a subsequent party for review. In some embodiments, during project initialization, project specifications and drawings are ingested. The project specifications and drawings are processed to extract relevant content, which is used to perform each submittal review. Artificial intelligence (AI) models are prompted through a series of dependent prompt engineered queries. The queries to the AI are broken into preconfigured pieces which improves the accuracy of the AI model. In order to break down the AI queries, the platform builds a program structure around making required calls to an AI API that are tuned for a particular program goal.

The invention discloses an accelerator automatic synthesis method, device and equipment, a medium and a product. The method comprises the steps of generating a candidate region set according to a global program structure tree corresponding to a target application program and LLVM intermediate representation; target areas needing to be accelerated are selected from the candidate area set based on preset constraint conditions, and a target area set is formed; and integrating and multiplexing the accelerators of each target area according to the structural similarity of each target area in the target area set to obtain the accelerator finally corresponding to each target area. According to the method, omission caused by manual selection in the prior art is effectively avoided, the overall performance optimization space is improved, the search efficiency is remarkably improved while the optimal performance is ensured, and the hardware resource cost is remarkably reduced.

To efficiently translate program code from a source language to a target language.SOLUTION: Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and the ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.SELECTED DRAWING: Figure 6

A platform that generates submittal reviews electronically for various construction projects. Embodiments include receiving project submittals by the reviewer, inputting the submittal in a web application interface, receiving the output in a specific format, reviewing the submittal review generated then downloading the reviewed submittal and handing it to a subsequent party for review. In some embodiments, during project initialization, project specifications and drawings are ingested. The project specifications and drawings are processed to extract relevant content, which is used to perform each submittal review. Artificial intelligence (AI) models are prompted through a series of dependent prompt engineered queries. The queries to the AI are broken into preconfigured pieces which improves the accuracy of the AI model. In order to break down the AI queries, the platform builds a program structure around making required calls to an AI API that are tuned for a particular program goal.

The present invention relates to the field of software testing technology, and discloses a test case generation method based on LLM and SMT solver. With search-type testing as the core driver, when coverage stagnation occurs, the execution path is semantically analyzed with the help of LLM, and a logical expression describing the target path condition is generated. Taking into account the limitations of LLM in logical reasoning, the system further introduces an SMT solver to verify and reason the logical expression output by LLM, and finally generates feasible test inputs, thereby improving test efficiency and coverage. The entire process forms a closed-loop optimization process that coordinates semantic understanding and logical solving. This method not only improves the efficiency and accuracy of path coverage, but also has good adaptability and versatility. It is applicable to multiple languages ​​and complex program structures, effectively improving the intelligence level and engineering practical value of automated testing. It effectively alleviates the problem of insufficient accuracy of large language models in complex path constraint reasoning.

The application relates to the technical field of software test automation, and provides a requirement-driven test case automatic generation method and system based on a semantic test graph, which comprises the following steps: performing multidimensional static analysis on source code, respectively constructing an abstract syntax tree, a control flow graph and a function call graph, and extracting function call relations and data flow dependency relations; constructing a semantic test graph by taking functions as nodes, taking function call relations as main edges and taking data flow dependency relations as auxiliary edges, and aggregating function nodes in the semantic test graph into several function modules; obtaining several test requirement nodes, calculating semantic similarity between the test requirement nodes and the function modules, establishing a mapping relationship from the test requirement nodes to the function modules, and forming a requirement-program structure association graph; and based on the requirement-program structure association graph, generating a test intention for each test requirement node, and converting the test intention into an executable test case. The highly corresponding relationship between the generated test case and the business requirement is ensured.

A program structure analyzer configured to analyze the structure of a program for causing a computer to execute a predetermined process identifies a source of an action corresponding to a starting pin associated with a reference port selected from one or more output ports set for a target block; repeats identification of a source with the identified source being set as the starting pin, until no output pin corresponding to an action executable by the target block is identified as a source; identifies an input port corresponding to an input pin receiving input from a source that is not an output pin corresponding to an action executable by the target block, as a parent port; and outputs, for each of the output ports set for the target block, dependence data indicating the parent port on which the output port depends.

An application migration method from client-server architecture to blockchain architecture is provided. The application migration method includes the following steps: S1: modeling an application program of a conventional client-server architecture and abstracting the application program to a program call tree; S2: defining a taint propagation rule for taint analysis by taking key data as tainted data, to acquire all taint variables in the application program; and S3: identifying a key module of the application program on the program call tree according to a tainted path, and setting a code refactoring rule to perform code refactoring on a method in the key module according to a special program structure.

The invention relates to a practical training device for electronic programming teaching, a turntable assembly comprises more than two groups of rotating rings which are coaxially and movably connected, and each rotating ring is uniformly provided with positioning grooves along the circumferential direction; the workpiece assembly is arranged in the positioning groove in any position, a sleeve is arranged at the movable tail end of the mechanical arm assembly, and the mechanical arm assembly is used for driving the sleeve to be transferred among the positioning grooves and collecting workpiece bodies; after the workpiece bodies are sequentially transferred to the inner cavity of the sleeve, the upper conductive part and the lower conductive part are conducted through the workpiece bodies arranged according to the correct sequence, and a timing stop signal is sent to the timer. According to the method, the time spent by an operator on the premise that the program compiled by the operator meets the requirement is quantified through the timer, and the quality of the program compiled by the operator is quantified according to the length of the time, so that students are stimulated to optimize the program structure.

This invention proposes a tabular construction method for program data structures, relating to the technical field of computer data structure construction. First, it determines the basic table structure corresponding to each basic data type, visually representing commonly used data types in computer programming in a tabular manner. This facilitates understanding and reading of the program structure and improves efficiency. Then, by nesting table structures within parent tables, multidimensional tables are obtained to express any data structure of varying complexity. This further clarifies the data structure based on a tabular approach, resulting in a low error rate in the output and making it easy to understand and communicate.

The application discloses a unit test code generation method, system, device and medium, which is applied to the technical field of code generation and includes the following steps: obtaining a structured meta-information set and a source code syntax structure model by performing program structure interface analysis and abstract syntax tree analysis on target engineering code; generating a structured context feature vector based on the structured meta-information set and the source code syntax structure model; searching a historical test case sample set from a historical test case database, wherein the historical test case sample set meets a matching condition in terms of global line coverage weighted similarity with the structured context feature vector; filling a Prompt template of different test sub-tasks based on the historical test case sample set and the structured context data through an Agent intelligent agent; generating different modular test codes by using different test code generation models; and performing closed-loop verification after integrating the different modular test codes into unit test codes, so that the code quality, practicality and adaptability are improved.

The invention discloses a RugPull detection method and system based on taint analysis and a large language model and a medium, and relates to the technical field of intelligent contract attack detection.The method comprises the following steps that S1, byte codes of an intelligent contract to be detected are obtained and preprocessed, the byte codes are converted into an assembly-level instruction sequence and analyzed, and the assembly-level instruction sequence is obtained; identifying the basic blocks and constructing a program structure chart; s2, a variable related to fund input is marked as a stain source and initialization is carried out, then the fund flow direction is analyzed, and a fund flow graph is constructed; s3, based on the fund flow graph, identifying suspicious asset flow logic, and generating a structured logic fragment; and S4, inputting the fund flow graph and the structured logic fragments into a large language model, and performing semantic reasoning and risk judgment. According to the method, byte codes can be analyzed without depending on source codes, and higher adaptability and practicability are achieved. Program structure analysis and language model semantic comprehension ability are fused, and RugPull risks in hidden complex logic can be accurately recognized.

The invention relates to a multi-component collaborative operation-based control method for ALD equipment, which comprises the following steps of: splitting the equipment into a plurality of components according to functions, newly establishing a corresponding functional block for each component, and respectively adding a plurality of actions for each functional block; declaring an enumeration data type; a variable is declarated in each function block; compiling action logic of each action; and calling each function block and the action of each function block in the main program according to the action logic of the equipment. The method has the advantages that the method is reasonable in design, equipment with a complex structure is split into a plurality of components, the action of each component is decomposed, and the action of each component is called according to the action logic of the equipment, so that the program structure is clear, the calling is convenient and flexible, and the program is convenient to maintain and update.

The invention provides a sensitive behavior dynamic reproduction method and device based on large model driving, and relates to the field of information security, and the method comprises the steps: generating an application program structure document according to an obtained development operation record of a to-be-detected mobile application program; performing feature analysis on the installation document of the to-be-detected mobile application and the application structure document to obtain a sensitive call chain; inputting a preset prompt word template and the sensitive call chain into a pre-constructed sensitive chain analysis model for analysis to obtain a script file corresponding to the sensitive call chain; and executing the script file, and verifying whether the sensitive call chain can dynamically reproduce the sensitive behavior or not. According to the method, accurate positioning of potential sensitive behaviors, automatic script generation and dynamic verification of the behaviors can be realized by combining static taint analysis with reasoning and generation capabilities of a large language model.

The invention discloses a programming method for batch processing of analog input signals based on a SIEMENS PLC controller. The programming method comprises the following steps: creating a user-defined PLC data type in a TIA Portal project tree; creating a data block, and establishing an array in the data block by taking a PLC data type as an element type; creating a function block, declaring a one-dimensional array of which form parameters are PLC data types in an interface area of the function block, and declaring a loop index variable, an intermediate conversion variable and an alarm temporary storage variable in a temporary variable area of the function block; executing cyclic batch processing logic in a program area of the function block; and calling the function block in the main program, and transmitting an array in the data block as an actual parameter through a pointer so as to complete batch processing of all analog input signals. The program structure is simple and clear, signal information is easy to search for field system maintenance personnel, the fault processing time is shortened, the system maintenance efficiency is improved, and the effect is remarkable.

This invention proposes a software product testing method, apparatus, equipment, and storage medium. The method includes: parsing the program structure of the software product to be tested, extracting module dependencies, and calculating structural complexity parameters; determining the behavioral statistical caliber based on the structural complexity parameters; dividing the running event sequence into statistical windows according to the behavioral statistical caliber, calculating the correction behavior intensity, and generating running behavior feature values; obtaining reference feature sequences from historical stable running phases, calculating the stable behavior reference center value and fluctuation width; and calculating the deviation score using the running behavior feature values, reference center value, fluctuation width, and behavioral statistical caliber to generate the testing result. This invention effectively solves the problem of insufficient accuracy of fixed threshold detection in complex software scenarios by adaptively adjusting the statistical caliber based on structural complexity and combining dynamic behavioral features with historical benchmarks for deviation determination, thereby improving the stability and reliability of software running status monitoring.

The application relates to the technical field of industrial automation control, in particular to a control program compiling method, an electronic device and a storage medium. According to the control program compiling method, a ladder program to be compiled is acquired first; the ladder program contains multiple types of element representation graphs; a structural analysis is performed on the logical connection relationship between the element representation graphs in the ladder program, so that tree program structure data is obtained; a program instruction mapping operation is performed based on the tree program structure data, so that multiple program assembly codes corresponding to the tree program structure data are obtained; and the multiple program assembly codes are translated and interpreted, so that a binary executable program is obtained. In this way, the operation efficiency, real-time performance and compilation process can be improved in the process of industrial automation control.

The present invention relates to a binary program analysis method, terminal device, and storage medium. The method comprises the following steps: S1: obtaining instruction set architecture features of the binary program; S2: obtaining a running start address by matching dump memory with the instruction set architecture features; S3: determining a read-only data area starting address and a read-only data area size based on continuously appearing constant character strings in the binary program; S4: determining a data area starting address based on a heap starting address and a read-only data area size obtained from a stack initialization code; S5: reconstructing the binary program into an executable binary program file based on the running start address, the read-only data area starting address, and the data area starting address of the binary program; and S6: performing decompilation analysis on the reconstructed executable binary program file using a decompilation tool. The present invention enables decompilation analysis of binary programs without an executable program structure.