28 results about "Type inference" patented technology

The invention discloses a non-standard configuration file analysis method, device and equipment and a storage medium, and is applied to the field of software engineering, a to-be-converted configuration file is read based on a configuration file path, and a target file type of the to-be-converted configuration file is determined based on a standardized identifier; constructing a rule base containing a separator rule, a type inference rule and a hierarchy identifier rule; analyzing the to-be-converted configuration file based on the rule base to obtain key value pairs of the target file type and a hierarchical structure between the key value pairs; and constructing a configuration file of the target file type based on the key-value pairs and the hierarchical structure between the key-value pairs, and calling a parser of the target file type to parse the configuration file. According to the method, the configuration files in various formats are uniformly converted into the configuration files in the standard format, the configuration files are analyzed through the analyzer in the standard format, an independent analyzer does not need to be developed for the configuration files in various formats, and the system development and maintenance cost and the version iteration risk are reduced.

Type definitions of user-defined types in application program code for which definitions are absent (“unknown types”) are inferred. A static analyzer implements two passes of a fixed-point type inference algorithm. Each pass encompasses a plurality of traversals of the application's control flow to build inferred definitions of unknown types until the inferred definitions are maximally built. To build an inferred definition, based on inferring a variable is an unknown type, the static analyzer infers member variables / functions of the unknown type based on contextual information associated with the variable. Type information of unknown types is propagated along control flow paths. After the first pass terminates, unknown types can be assigned known types based on matching of inferred definitions. Inferred definitions of remaining unknown types are incorporated into the application program code. A second pass of type inferencing and data flow analysis are then performed with the inferred definitions incorporated therein.

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 present application discloses a method and device for inferring the type of a code snippet that integrates constraints and statistics. The method includes: using a constraint-based method to perform type analysis on the current code snippet according to preset type constraints and an initial API knowledge base to obtain an initial constraint inference type; enhancing the context of the current code snippet through the initial constraint inference type to obtain an enhanced code sequence; using a statistical method to perform type analysis on the enhanced code sequence to obtain a list of element candidate types; replacing the initial API knowledge base with a reduced API knowledge base, returning to the constraint inference step until the iteration termination condition is met, determining the type of the code snippet based on the obtained optimized constraint inference type and optimized statistical inference type to obtain a target inference type. The present application can solve the technical problems that the existing constraint-based method has high requirements for code quality and has limited adaptability, and the statistical-based method lacks pertinence and has a low accuracy rate.

The invention discloses a closed-source contract attack risk confirmation method based on pre-execution and type inference, and the method comprises the steps: triggering detection when related transactions of a closed-source intelligent contract appear in a block chain network, and achieving the pre-execution through deploying the contract in a local virtual machine in advance; performing preliminary screening on the contracts, detecting suspicious features and preferentially analyzing high-risk contracts; and recovering intelligent contract information by using a type inference technology, exploring a program state through a fuzzy test, collecting contract execution characteristics in an execution process, and finally performing risk assessment. Through combination of pre-execution and dynamic type inference, the technical bottleneck that closed-source contract attack risk confirmation lacks source codes and type information is broken through, early warning and active defense of malicious contracts are realized, and a security technical guarantee is provided for a block chain.

The invention discloses a Java program analysis-oriented intermediate representation construction method, which comprises the following steps of: performing single assignment form conversion on a Java bytecode to be processed to obtain a single assignment Java bytecode, and constructing a typeless intermediate representation through an abstract interpretation translation algorithm based on the single assignment Java bytecode. Constructing a type constraint and a type constraint graph by using the typeless intermediate representation based on a Java type check rule, and solving the type constraint graph by using a pruned lattice algorithm to obtain a typeless intermediate representation; according to the method, a common byte code mode is well utilized through single assignment transformation and a pruning-based lattice algorithm. Java byte codes based on a stack type virtual machine are converted into typeless three-address code intermediate representation based on a register virtual machine based on abstract interpretation, and generation of redundant statements is avoided as much as possible. And a pruning lattice algorithm is adopted, so that the type of an unknown type variable can be accurately and quickly determined, and a type reasoning result is ensured to be safe.

The present application provides a method and apparatus for type inference in a programming language. The method comprises: acquiring an abstract syntax tree of source code, wherein the abstract syntax tree comprises a first node and a subtree of the first node, the first node indicates a first field in the source code, and the first field is one of the following: a variable, a class, a function or a parameter; constructing or updating a full type tree on the basis of the abstract syntax tree, wherein the full type tree indicates the types of a plurality of nodes in the abstract syntax tree and the relationship between the plurality of nodes, and the types of the plurality of nodes are determined on the basis of at least one of the following: the type of a second node in the plurality of nodes is indicated by the abstract syntax tree, and the type of a third node in the plurality of nodes is obtained from a type map, or the type of a fourth node in the plurality of nodes is based on the type of a fifth node, and the type of the fifth node is indicated by the abstract syntax tree or obtained from the type map, wherein the type map indicates the mapping relationship between at least one declaration and at least one type; and outputting an inference result on the basis of the full type tree, wherein the inference result comprises the type of the first node. The solution of the present application facilitates quick and accurate type inference.

The invention belongs to the field of urban calculation and intelligent transportation, and discloses a hierarchical fine-grained human trajectory activity type inference method and related devices.The method comprises the steps that firstly, rigid activity types are recognized through predefined anchor point rules, and efficient labeling of regular segments is ensured; subsequently, the remaining staying segments mark transactional or leisure motivations through a motivation classification model to differentiate internal heterogeneity of non-rigid activities; and finally, performing structured inference on the marked fragment by a specific large language model of the motivator, and outputting a fine-grained non-rigid activity type. By the adoption of the method, the overall accuracy and robustness of fine-grained activity inference are remarkably improved, the performance is optimized especially on identification of non-rigid activities, adaptability to complex multi-constraint scenes is enhanced, and reliable fine-grained classification requirements are supported.

The application discloses a low-code platform variable type automatic inference method and device. The method comprises the following steps: screening a variable with an automatic type identifier in a low-code platform to obtain a target variable; obtaining a default value inference result based on a type inference rule; traversing a structured model corresponding to a business logic and declaration information to obtain a node assignment inference result; obtaining a dependency relationship inference result through a multi-round iteration strategy recursive derivation; integrating the default value inference result, the node assignment inference result and the dependency relationship inference result to determine an initial type of the target variable; storing the initial type based on inference trace information and realizing change notification to finally obtain a target type. Through multi-stage inference, expression type calculation, chain dependency processing and other designs, the application does not need to manually specify the type, supports complex scene inference, maintains type safety, reduces use and maintenance costs, and improves the development efficiency and ease of use of the low-code platform.

This invention discloses a training and inference method for complex type inference at the Python repository level. It constructs a semantic graph of types; organizes type semantic contexts and constructs single-round type inference training samples, performing supervised fine-tuning on a large language model to obtain a single-round type inference base model; represents the predicted type and reference type as a type tree, constructing verifiable structured rewards; constructs multi-round refinement objectives according to the depth of the reference type, aggregating round-by-round refinement rewards, termination penalties, and final result rewards to obtain a hierarchical reinforcement learning model; constructs a repository-level scheduling order; for the multi-round refinement process, in each round, multiple candidate outputs are mapped to a depth-constrained abstract type space, and the stage decoding result is obtained through type space self-consistency aggregation, outputting the final predicted type. This invention can simultaneously enhance the cross-process semantic utilization capability, deep structure inference capability, and repository-level type consistency in complex type inference.

The invention discloses an automatic inference method and device for low-code platform variable types. The method comprises the following steps: screening and matching variables with automatic type identifiers in a low-code platform to obtain target variables; obtaining a default value inference result based on the type inference rule; traversing the structured model and the declaration information corresponding to the service logic to obtain a node assignment inference result; recursive derivation is carried out through a multi-round iteration strategy to obtain a dependency relationship inference result; integrating the default inference result, the node assignment inference result and the dependency inference result, and determining the initial type of the target variable; and storing the initial type based on the deduced traceability information and realizing change notification, and finally obtaining a target type. Through multi-stage inference, expression type calculation, chain dependency processing and other designs, a user does not need to manually specify a type, complex scene inference is supported, type safety is kept, the use and maintenance cost is reduced, and the development efficiency and usability of a low-code platform are improved.

The invention discloses a dynamic language type inference method and system based on a large language model, and the method comprises the steps: obtaining a cross-function context dependency relationship of unlabeled variables through constructing a structured knowledge base containing a user definition type and a third-party library type in combination with an inter-process code slicing technology; based on structure information implied in the slices, candidate data types with similar structures are retrieved and screened from a knowledge base, the candidate data types and code contexts are jointly constructed into natural language prompts, the natural language prompts are input into a large language model, and multiple types of prediction results are generated; and determining a final type inference result by performing frequency sorting on prediction results and combining a name similarity matching and structure mapping mechanism. According to the method, the context understanding capability of the large language model is enhanced through static analysis, and the accuracy and robustness of type inference in the dynamic language are effectively improved.

The application discloses a source code vulnerability detection method based on multi-source feature fusion and ensemble learning, which adopts a three-stage pipeline architecture: firstly, four-way features of abstract syntax tree, control flow graph, data flow graph and sequential semantics are extracted from the source code in parallel and encoded respectively; secondly, the four-way features are dynamically assigned weights and weightedly fused through a multi-head attention mechanism, and a limited-depth inter-function dependency graph is constructed to complete the inter-process context of cross-function, and then the context-enhanced multi-source fusion vector of 512 dimensions is output through the transformation coding and principal component analysis dimension reduction; finally, the three vulnerability type exclusive sub-models of syntax, data flow and control flow are driven by the fusion vector for parallel integrated detection, the contribution of each sub-model is quantified by combining gradient importance analysis, and the integrated decision layer dynamically weights the output of the detection label, vulnerability confidence and the explainability report containing the main trigger sub-model, key feature dimension and CWE vulnerability type inference.

Controlling data schema is provided for data object(s) in a data oriented architecture. Suggestions are provided for received data objects, including determining whether the received data object maps to one of multiple data types, and performing a conditional type inference to provide the one or more suggestions based on a result of the determining of whether the one of the data object maps to one of the multiple data types.

Provided in the present application are a method and apparatus for type inference of a programming language. The method comprises: in the process of developing program codes, acquiring a node to be inferred in an abstract syntax tree, wherein said node indicates a field to be inferred in the program codes, said field comprising one of the following fields: a variable, a class, a function, or a parameter; acquiring a constraint related to said node, wherein the constraint is used for indicating a relationship involved in said node in the abstract syntax tree; and on the basis of the constraint, determining the type of said node, wherein the type of said node is one of the following: a numeric type, a sequence type, a mapping type, a set type, an iterator type, a context manager type, a module type, a meta type, a null value type, or a callable type. The solution of the present application is conducive to reducing the memory usage of type inference.

The invention belongs to the related technical field of network security, and particularly relates to a web form weak password detection method based on cooperation of a static feature library and AI dynamic analysis, which comprises the following steps of: constructing a web form structure based on the static feature library containing various login features of various web services and a form structure tree; or identifying the field type according to the standard form, judging whether a keyword of which the language is matched with the language of the field can be found in the multi-language keyword mapping table or not when detecting that different attribute value conflicts exist in one field, if not, analyzing the framework feature layer, and if the type still cannot be analyzed, analyzing the type through the AI reasoning layer; integrating visual features, spatial relationships and context semantics which are not covered by the first two layers through a deep learning model, realizing field type inference in a complex scene, and constructing a web form structure to be detected based on each field type; and generating an HTTP (Hyper Text Transport Protocol) form request message based on the constructed web form structure.

The invention belongs to the technical field of typed reasoning, provides a large model typed reasoning method based on hierarchical distillation and dynamic weight merging, and realizes structured migration and cross-task fusion of SLMs typed reasoning knowledge through hierarchical thinking chain distillation and dynamic weight model fusion. The hierarchical distillation is used for extracting three types of key information including semantic representation, inference intermediate state and final thinking chain expression from the LLMs, and establishing semantic basis, logic arrangement and step generation capability required by typed inference in the SLMs in a structured mode. Meanwhile, a dynamic weight merging strategy with task types as guidance is adopted, adaptive fusion is carried out on parameter subspaces of the trained SLMs, the complementary advantages of different reasoning types are effectively reserved, and the cross-type reasoning capability is enhanced.

The application discloses a kind of self-adapting inference method and system based on dynamic thinking network and iterative routing agent, comprising: step 1, input and initialization are carried out;Step 2, complexity classification and routing decision are carried out;Step 3, directly solve branch, directly infer simple problem and cache result;Step 4, recursive decomposition branch, complex problem is carried out multi-level decomposition and generates subtask;Step 5, intelligent termination judgment is carried out, in recursive processing process, two kinds of termination conditions are monitored in real time;Step 6, sub-answer aggregation, verification and final output are carried out.The application realizes the adaptive routing mechanism based on input problem complexity, breaks through the fixed structure limit of traditional chain or tree type inference, and realizes the accurate allocation of computing resources.

The invention discloses a method, a device and equipment for optimizing the performance of an expression in a graph database, relates to the field of graph database optimization, is used for optimizing the expression in a Nebula graph database, and is characterized by comprising the following steps: acquiring a query request of a user, and transmitting the query request to a query processing module in the graph database; the query processing module performs lexical and grammatical analysis on the query request through the analyzer module, and converts the query request input by a user into a typeless expression; transmitting the typeless expression to a verifier module, performing type derivation, and converting the typeless expression into a typed expression. According to the main technical scheme and effects, by improving the life cycle management of the expression, the type derivation and runtime calculation mechanism of the expression is designed, the calculation efficiency is improved, the primitive data is analyzed and used for expression form conversion, the strong type expression is more efficient, and the expression life cycle and a code implementation framework are clearly defined.

This invention provides a real-time Python API recommendation method based on context analysis. It features the following steps: first, extract call points within the Python code context; then, perform type inference on each call point. If the inference is successful, the inferred type callable method is used as the API candidate set; otherwise, a set of API candidates is generated from standard library APIs, third-party library APIs, and APIs defined in the current context; then, extract data flow sequences containing only true positives based on five constraints: assignment operations, loop structures, attribute access / calls, container access, and function parameter passing; then, through context analysis, collect three features: data flow sequence, token similarity, and co-occurrence rules, and encode these features into feature vectors; then, label the feature vectors and train all labeled vectors using a random forest model; finally, based on the trained recommendation model, the recommendation results are ranked by probability score and presented to the developer.

This invention discloses a method and system for optimizing decompiled smart contract code, addressing the technical problem that existing smart contract decompilation code optimization methods cannot provide more comprehensive optimization solutions. The method includes: acquiring the decompiled code of the smart contract to be optimized; constructing a contract dependency graph based on the decompiled code; extracting fragments from the contract dependency graph to generate variable and method code contexts; generating multiple thought chain hints based on the contract dependency graph; using a large language model to optimize the decompiled smart contract code based on the variable and method code contexts, multiple thought chain hints, a pre-set variable type inference candidate set, and a pre-set contract attribute inference candidate set, outputting intermediate decompiled smart contract code; and using pre-set program analysis and verification technology to perform a consistency check on the intermediate decompiled smart contract code based on the decompiled code of the smart contract to be optimized, determining the target smart contract decompiled code.

The invention discloses a CodeBERT-based JavaScript global identifier conflict static detection and repair method and a CodeBERT-based JavaScript global identifier conflict static detection and repair system, which are characterized in that a perfect JavaScript code extraction and global identifier detection rule is designed, and a CodeBERT model is utilized to extract function type information from an API (Application Program Interface) document and a designed type inference rule to make type inference so as to detect type conflicts; and finally, predicting a correct identifier actually wanted to be referenced by a developer at a conflict position through an original pre-training target of CodeBERT to repair the conflict, ensuring the accuracy of conflict detection and repair without introducing extra performance overhead during operation, and helping the developer to discover and repair a global identifier conflict in a code development stage. According to the method, the global identifier conflict is fundamentally solved, and the conflict is not merely detected or isolated.

The application provides a type inference method and device of a programming language, the method comprising: in the development process of program code, obtaining a node to be inferred in an abstract syntax tree, the node to be inferred indicating a field to be inferred in the program code, the field to be inferred comprising one of the following: a variable, a class, a function or a parameter; obtaining a constraint related to the node to be inferred, the constraint being used to indicate a relationship involved by the node to be inferred in the abstract syntax tree; determining a type of the node to be inferred according to the constraint, the type of the node to be inferred being one of the following: a numeric type, a sequence type, a mapping type, a set type, an iterator type, a context manager type, a module type, a metatype, a null type or a callable type. The scheme of the application is beneficial to reducing the memory occupation of type inference.

This application discloses a target element localization method and apparatus based on interface structure perception, relating to the fields of artificial intelligence and human-computer interaction. By fusing multiple inference results, it achieves collaborative optimization based on explicit structural inference and multi-source information, which not only significantly improves the localization accuracy under complex interface layouts and complex natural language commands, but also enhances the interpretability and generalization ability of the method, providing more accurate target element localization capabilities for graphical user interface agents. The method includes: in response to a target element localization request, acquiring interface input data and natural language commands; constructing an interface relationship graph based on the interface input data; performing multi-type inference on element objects in the interface relationship graph to obtain multi-type inference results for the element objects; selecting at least two inference results from the multi-type inference results for fusion, and determining the localization result of the target element based on the fusion result.

This invention discloses a verification-based method for inferring Python function parameter types. The method involves parsing the input Python source file to extract function and parameter information; constructing an initial set of candidate parameter types; identifying the behavioral characteristics of function parameters within the function body based on static analysis and filtering the candidate type set accordingly; adding type annotations to the function parameters and introducing a static type checker to verify the candidate types; and integrating the verified types to obtain the final parameter type inference result. By introducing a Python static type checker into the type inference process, this invention effectively improves the accuracy and comprehensiveness of function parameter type inference results. It can reliably infer Python function parameter types even in the absence of call point context or explicit type annotations, overcoming the inaccurate and incomplete inference results of existing methods, and has high engineering practical value.

The invention discloses a WebAssessment type inference method and device based on an enhanced semantic map, and a storage medium. Firstly, deep memory access semantics are extracted from WebAssembly binary codes, then an enhanced semantic graph containing access mode clustering, dependency chain analysis and type propagation paths is constructed, finally, the semantic graph is converted into high-precision type definition and data structure layout, and accurate inference of a complex data structure is achieved. Specifically, deep semantic association can be found from a seemingly irrelevant memory access sequence, and a logic structure of an original program is reconstructed. According to the method, through the construction of the enhanced semantic map, a complete definition including a nested structure, a complex, an array and other complex data types can be accurately identified and reconstructed, and the reverse analysis precision of WebAssembly codes is remarkably improved.

Type definitions of user-defined types in application program code for which definitions are absent (“unknown types”) are inferred. A static analyzer implements two passes of a fixed-point type inference algorithm. Each pass encompasses a plurality of traversals of the application's control flow to build inferred definitions of unknown types until the inferred definitions are maximally built. To build an inferred definition, based on inferring a variable is an unknown type, the static analyzer infers member variables / functions of the unknown type based on contextual information associated with the variable. Type information of unknown types is propagated along control flow paths. After the first pass terminates, unknown types can be assigned known types based on matching of inferred definitions. Inferred definitions of remaining unknown types are incorporated into the application program code. A second pass of type inferencing and data flow analysis are then performed with the inferred definitions incorporated therein.

The present disclosure provides a code generation method, device, electronic device and storage medium, which relate to the field of computer technology. The method includes: obtaining a logical directed graph; converting the logical directed graph into a tree structure; converting the tree structure into a recursive structure; the recursive structure includes a branch microstructure and a linear microstructure; converting the branch microstructure and the linear microstructure into a program language structure; extracting a variable relationship directed graph corresponding to the logical directed graph; the variable relationship directed graph includes variables with determined variable types; performing variable type inference based on the variable relationship directed graph to obtain all variable types in the logical directed graph; based on scope identification rules, identifying the scope of the variables corresponding to the branch microstructure and the linear microstructure; generating a code file corresponding to the logical directed graph based on the program language structure, the logical flow directed graph, the variable type and the scope of the variable. The present disclosure can improve the efficiency and accuracy of converting the logical directed graph into a code file.