561 results about "Code segment" patented technology

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

The embodiment of the invention provides a code repairing method, device and equipment based on a large language model.The method comprises the following steps that a code to be repaired is analyzed through a code scanning tool on the basis of a coding specification, error information of the code to be repaired is obtained, the error information comprises an error number, a violation rule, a supplementary description, an error example, a correction example, an error type and an error code segment; generating a cue word template according to a historical repair sample and a coding specification of the large language model, and filling the cue word template with the error information to generate a filled cue word; and inputting the filled cue word into the large language model to generate a repair code segment, judging whether the repair code segment accords with a set conversion specification, and if the repair code segment accords with the conversion specification, replacing the code to be repaired with the repair code segment to generate a repaired code. According to the scheme, the code is repaired through an end-to-end automatic process, and the code repairing efficiency is improved.

The invention relates to a computer code vulnerability detection method and system based on artificial intelligence, and the method comprises the steps: obtaining a detection demand input by a user, analyzing the detection demand based on a natural language processing algorithm, and extracting a target source code set corresponding to the detection demand from a to-be-detected code warehouse; performing standardization processing on the target source code set, and constructing a code feature data set; inputting the code feature data set into the trained vulnerability detection model, identifying to obtain a vulnerability code snippet, and generating a vulnerability score and a vulnerability type; and grading the vulnerability code snippets according to the vulnerability scores, generating repair suggestions in combination with vulnerability types, and outputting a corresponding detection report which comprises risk levels and the repair suggestions. The method has the effect of improving the accuracy of vulnerability detection.

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

Disclosed in the present application is a code generation method, which is applied to a code development platform. The code development platform comprises a main plug-in and a plurality of extension plug-ins, wherein the main plug-in is used for providing an actual value of a basic variable of a prompt, and the plurality of extension plug-ins are used for extending the prompt in terms of a programming language, scenario or development framework. The method comprises: a main plug-in receiving a code generation instruction, and then determining, in response to the code generation instruction, at least one extension plug-in corresponding to the code generation instruction from among a plurality of extension plug-ins; the main plug-in receiving a prompt returned by the at least one extension plug-in, wherein the prompt is obtained by means of the at least one extension plug-in extracting an actual value of an extension variable on the basis of extraction logic and replacing an extension variable in a prompt template with the actual value of the extension variable; the main plug-in performing inference by means of a language model and on the basis of the prompt, so as to obtain a second code segment. In the method, a prompt of a specific code generation task and the implementation of prompt engineering is entrusted to corresponding extension plug-ins, thereby realizing flexible extension of prompts.

The invention discloses an intelligent completion code optimization method based on texture pre-rendering and multi-level cache, which comprises the following steps of: pre-establishing a grammar rule set and a GPU cache characteristic database, storing a grammar style sheet when a code editor is started, generating a font image set and loading current GPU cache parameters; after a candidate code segment list pops up through intelligent completion, dividing candidate code segments into data blocks, analyzing syntax types, determining character styles, and generating candidate code segment textures through instantiation rendering and a fragment shader; according to the current GPU cache parameter, the selected priority of the candidate code segment and the compressed volume, the texture is distributed to a first-level buffer, a second-level buffer or a global video memory according to a rule, and metadata is recorded and added into a global texture cache table; and after the user selects the candidate code segment, reading texture data from the corresponding GPU buffer, calculating a display coordinate by combining a cursor row and column index, an editor basic style parameter and a DPI scaling, completing texture rendering and drawing, and realizing optimized display of the intelligent completion code.

The invention discloses a vulnerability detection method and system based on abstract syntax tree decomposition and large language model assistance. The vulnerability detection method and system are used for solving the problem that an existing pre-training model is insufficient in detection accuracy under complex code logic and multiple execution paths. The method comprises the following steps: firstly, analyzing a code snippet into an abstract syntax tree, splitting the abstract syntax tree into a plurality of sub-trees through an improved decomposition algorithm, and combining each sub-tree with a natural language annotation generated by a large language model to form an abstract sub-tree with the annotation; then, a semantic aggregator based on Transform is used for modeling the relation between the sub-trees, features are fused to a target vulnerability vector, and finally, vulnerabilities are predicted through a classifier. Based on the technical scheme, the vulnerability detection accuracy is effectively improved, and the performance of the vulnerability detection model is greatly improved.

The invention relates to the technical field of code development, and provides an AI code effective proportion statistical method and device, a medium and equipment. The method comprises the steps of obtaining related information of codes submitted by a user; according to a user name in the related information, searching log information of an AI code generated by a corresponding user through adoption of a code generation tool; under the condition that the file name of the code submitted by the user is matched with the file name in the log information, searching a corresponding submitted code segment from the code submitted by the user according to the mark information in the log information; and calculating the similarity between the AI code and the submitted code segment, and counting the effective proportion corresponding to the AI code according to the similarity. Therefore, the calculation of the effective proportion considers the quality of the AI code, so that the effective proportion can accurately reflect the real contribution of the AI code, and the calculation process is automatically realized, thereby avoiding the tedious, time-consuming and labor-consuming conditions of manual labeling.

Techniques for generating proprietary software code using large language models (LLMs) are disclosed. An LLM is trained on billions of words, tokens, and code segments to generate non-proprietary software code. A system uses the LLM to generate proprietary software code by generating a set of LLM prompts and a proprietary code mapping. Based on receiving an instruction to generate a set of software code, a system generates a set of LLM prompts. The system prompts the LLM to generate a set of non-proprietary software code. The system further prompts the LLM to generate a set of pseudocode from the non-proprietary software code. The system further prompts the LLM to generate proprietary software code from the pseudocode and the proprietary code mapping.

A computing platform provides code vulnerability detection and evaluation. The computing platform may use a quantum graph categorizer along with quantum polymorphism execution channels to reduce false positives and provide optimized real-time vulnerability evaluation of code and code segments. The computing platform may use a hybrid approach comprising a mix of static and dynamic vulnerability validation and real-time parallel technique execution.

A method of code vulnerability detection includes obtaining a set of patches from at least one code repository for a vulnerability record of a vulnerability of a software component, applying the set of patches to obtain a modified set of code files modified by the set of patches, and inferring a vulnerable code section set according to the modified set of code files. The vulnerable code section set is a subset of the modified set of code files. The method further includes obtaining a vulnerable version list of a plurality of versions of the software component comprising the vulnerability, validating a version in the vulnerable version list as having the vulnerable code section set, and processing a target software comprising the version using the vulnerable code section set.

The invention provides a code compiling method and device based on a P4 assembler, the assembler and a medium. The code compiling method comprises the steps that a to-be-processed assembly code file is obtained; splitting and compiling the assembly code file to obtain a plurality of code segment compiling results; according to the auxiliary information, by taking Parser components, Mat components and Deparser components contained in the Ingress unit and the Egress unit as classification types, classifying the code segment compiling results to obtain code segment groups; linking the P4 table item data and the machine instruction code contained in the code segment group, and endowing a link result with a global instruction identifier and code segment header information to obtain a code segment binary code; and generating a firmware file based on the code segment binary code. The compiling process of the network processor can be simplified, so that the development efficiency is improved.

The invention provides an unreachable code identification method and device, electronic equipment and a storage medium, and is suitable for the fields of financial science and technology, medical service and software code detection. The method comprises the steps of obtaining a target business code file; performing grammar analysis on the target service code file to obtain a target grammar structure tree; by traversing the target grammar structure tree, identifying an unreachable code segment on a static level as a first candidate unreachable code segment; running the business code file in a preset running scene, obtaining an effective code path in the running process in real time, and determining a code segment which is not covered by the effective code path in the target business code file as a second candidate unreachable code segment; and performing comparative analysis on the first candidate unreachable code segment and the second candidate unreachable code segment, and determining a target unreachable code segment in combination with a preset unreachable judgment rule. According to the invention, the accuracy and the recognition rate of unreachable code recognition can be improved.

The invention relates to a machine question answering technology, in particular to a code generation and correction method based on multilevel context awareness and memory iteration, which comprises the following steps of: S1, receiving a preliminary fuzzy demand, proposing a clear question and generating a structured demand document according to content supplement and question answer; s2, learning the context of the item and generating an initial code according to a learning result and the structured demand document; s3, running the initial code; s4, monitoring operation, capturing logs, if the operation is successful, ending circulation, and if the operation is not successful, entering S5; s5, analyzing the log and positioning errors; updating the error to a problem pool and a correction history, and triggering a correction request; the positioning error comprises extraction abnormity, files and line numbers; the correction request comprises an error code snippet, error information and a correction history; and S6, analyzing the correction request and generating a correction code, wherein the correction code comprises construction of an enhanced diagnosis prompt word. According to the invention, rapid, efficient and accurate project code generation can be realized.

The invention discloses a code similarity detection method and system based on a large language model, and the method comprises the steps: obtaining a to-be-detected source code pair, and marking the to-be-detected source code pair as a source code A and a source code B; performing code cleaning and format standardization on the source code A and the source code B, and mapping a variable name and a function name which are customized by a user into a uniform placeholder; analyzing the source codes A and B based on the abstract syntax tree, respectively replacing variable names and function names in the source codes A and B with unified serialized placeholders, and maintaining a mapping table; meanwhile, expanding a lexical dictionary of the pre-training large language model, and inserting a special identifier; splicing the replaced code snippets with special identifiers, and constructing a structure sensing input sequence; inputting the structure perception input sequence into a pre-trained large language model backbone network for feature coding to obtain a high-dimensional semantic feature vector containing global context information; connecting a multi-task prediction head behind the large language model backbone network, inputting the feature vector into the multi-task prediction head, outputting probability distribution of code clone types through a classification task head, and respectively outputting a row level similarity score and a lexical element level similarity score through a regression task head; and according to the classification probability and the regression score, performing comprehensive judgment by combining a preset threshold, and generating a detection report. According to the method, similar codes after variable renaming, statement rearrangement or control flow transformation can be accurately recognized, and the accuracy and robustness of code similarity detection are improved.

Embodiments of the present application provide a method and device for compiling program code. The method for compiling program code may include: obtaining source code; and performing a compilation operation to obtain a first compilation result of the source code, where a first code segment in the source code is compiled into machine code in the first compilation result, a second code segment in the source code is compiled into bytecode in the first compilation result, and the first compilation result is configured to be deployed to a target environment. The proposed technical solutions may be conducive to ensuring performance at low storage resource utilization.

The invention discloses an interactive programming data generation method which is used for the technical field of large language models. The method comprises the following steps: receiving a user demand carrying a target code unit, and obtaining a directed graph formed by resource information, the code unit and a dependency relationship thereof in a running environment; screening related resources based on demands to generate a target resource integration text used for describing resource application conditions, and extracting a sub-graph associated with a target code unit through a directed graph to generate a target code segment; combining historical dialogues, resource integration texts and code segment arrangement cues, and guiding the large language model to output prediction information including codes, interpretations or tool calling instructions. According to the method, by dynamically integrating the environment context and the code dependency relationship, it is ensured that the generated code is matched with the actual working space of the user, multi-round dialogue iterative optimization is supported, and the accuracy and practicability of the intelligent agent in interactive programming are improved.

Various embodiments of the present disclosure include systems, methods, and / or apparatuses for executing an agentic-AI-based software modernization system. An example system may be configured to receive one or more portions of monolithic legacy code of a legacy software program product, the monolithic legacy code comprising a plurality of code segments, group the monolithic legacy code by functionally related code segments of the plurality of code segments to define a plurality of groups, generate an API for each identified group, generate modern microservice code for each identified group to define a modernized software program product, and validate the modernized software program product.

The invention relates to a system calling ptrace-based operating system micro-service security protection system and method, which are characterized in that the running state of micro-service is monitored in real time through calling of a ptrace system in a user state, and dynamic integrity verification is realized in combination with an SM3 hash algorithm. The method comprises the following steps: calculating an SM3 hash value of a code segment of a micro-service when the micro-service is started, storing the SM3 hash value as a static reference, periodically reading memory data of a target process through ptrace in a running process, recalculating a dynamic hash value, and comparing the dynamic hash value with a reference value; if it is detected that the hash values are inconsistent, it is judged that code injection attacks exist, and a defense mechanism is triggered. According to the method, the system stability risk caused by kernel module injection is avoided, meanwhile, real-time interception of tampering attacks during operation is achieved through high-frequency dynamic comparison, the lightweight design enables the method to be suitable for high-reliability scenes such as spaceflight with limited resources, the integrity of a micro-service code segment can be guaranteed, low performance loss of the system can be maintained, and the system reliability is improved. The balance blank between dynamic defense and system stability in the prior art is effectively filled.

The embodiment of the invention provides a model-based application building method and device, equipment, a storage medium and a program product. The method comprises the steps that demand description information for an application is determined based on interaction information related to generation of the application, and the demand description information indicates requirements for the application; determining at least one service matched with the demand description information from a plurality of created candidate services based on the demand description information; and generating a code for implementing the application at least based on the demand description information and the service encapsulation information corresponding to the at least one service, the code comprising a code segment for calling the at least one service. According to the application building of the embodiment of the invention, a machine learning model can be utilized. For example, determination of demand description information, determination of matching services, and code generation may utilize a large model. Therefore, the code generation efficiency in application building can be improved.

The invention discloses a program defect repairing method and device, equipment and a storage medium. The method comprises the steps that a defect code segment is obtained from a first program code which is reported to be wrong, and a similar defect code segment and a similar repair patch corresponding to the defect code segment are inquired in a historical code library; constructing a first prompt word based on the defect code segment, the similar defect code segment and the similar repair patch, generating a repair patch based on the first prompt word, and repairing the first program code through the repair patch to obtain a second program code; and under the condition that the verification of the second program code is not passed, regenerating a repair patch based on a defect code segment appearing in the verification process of the second program code, and repairing the second program code through the newly generated repair patch. According to the method, the big language model is guided to generate the high-credibility repair patch through the historical similar defects and the repair patch thereof, and the problem that in the prior art, the repair patch is unreliable due to the fact that the big language model is separated from an actual repair case is solved.

The invention discloses a debugging method and device for a low-code platform, a storage medium and electronic equipment. The method comprises the steps that a low-code engine is started in a local environment, the low-code engine is used for calling a target code to provide a low-code platform service, and the target code represents a modified original low-code segment; under the condition that the target code is used for transforming the front-end service, request flow data is obtained through a flow forwarding component, breakpoint debugging operation is executed based on the request flow data to obtain a debugging result, and the request flow data is used for indicating an interaction request obtained from a front-end page; the traffic forwarding component is used for controlling the request traffic data to be forwarded from the test environment to the local environment; under the condition that the target code reforms the back-end service, a target interface is used for executing breakpoint debugging operation to obtain a debugging result, and the target interface is used for debugging the target code in the local environment. According to the method and the device, the technical problem that the debugging efficiency is relatively low when the modified original low-code segment in the low-code platform is debugged is solved.

Systems, computer program products, and methods are described herein for synthetic intrusion data generation and remediation via machine learning. The present disclosure includes training a first machine learning model using a plurality of malicious code segments from a code repository, generating, using the first machine learning model, a predetermined number of generated code segments, training a second machine learning model using the plurality of malicious code segments, generating, by using the second machine learning model, at least one generated heuristic mitigation resource for the generated code segments, analyzing, via a static heuristics analysis, stored code on an endpoint device, and applying the at least one generated heuristic mitigation resource upon a first condition wherein the static heuristics analysis identifies a malicious activity.

The invention discloses an intelligent contract vulnerability detection method based on hierarchical multi-granularity coding, and the method comprises the steps: dividing an intelligent contract source code into multi-level data for feature extraction, and determining a token feature corresponding to each token, a statement block feature corresponding to each statement block, and a cross-statement block feature corresponding to each code segment; according to the token feature of the token and the statement block feature corresponding to the statement block, determining an intra-segment feature corresponding to each code segment, according to the cross-statement block feature, determining a cross-segment feature, and according to the intra-segment feature, the cross-segment feature and the document-level feature, determining a vulnerability detection result, therefore, a unique hierarchical structure is divided according to the code features so as to facilitate feature extraction of the vulnerability detection model, and an interaction relationship and a dependency relationship among the token, the statement block and the code segment can be obtained through feature extraction and feature fusion of the vulnerability detection model, so that the accuracy of intelligent contract vulnerability detection is improved.

The invention discloses a source code reconstruction method and device, equipment and a medium. The method comprises the following steps: acquiring a to-be-processed source code and reconstruction performance description content; determining at least one target code snippet in the to-be-processed source code according to a code reconstruction standard; according to the to-be-processed source code, generating a calling relation and an input and output relation corresponding to the to-be-processed source code; aiming at each target code snippet, reconstructing the target code snippets according to the calling relation and the input and output relation to obtain reconstructed code snippets; and replacing the target code snippets in the to-be-processed source codes with the reconstructed code snippets, wherein the to-be-processed source codes before replacement and the to-be-processed source codes after replacement have the same functions. According to the embodiment of the invention, the source code upgrading efficiency and automation degree of the original system can be improved.

The invention provides a user interface testing method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the following steps: acquiring a target test script, and performing semantic conversion on an operation instruction in the target test script by utilizing a first model to obtain a target test step set containing business semantic description; the target test step set is executed, in the process of executing the target test step set, the first model is used for determining the positioning mode of the target interface element according to the business semantic description, corresponding interaction operation is executed, and actual interface output is obtained; in response to execution failure of the target test step set, generating a diagnosis result by using a first model at least based on the actual interface output and the business semantic description; and inputting the diagnosis result into a second model, generating a modification code snippet by the second model, and applying the modification code snippet to the target test script.

The invention discloses a method and a device for generating a software vulnerability analysis report, and relates to the field of information security. The method comprises the following steps: acquiring auditing information of software vulnerabilities; a code feature matching rule for the software vulnerability is generated according to the auditing information through the large language model, and the code feature matching rule is used for matching a code calling mode related to the software vulnerability in the target software project; constructing a calling graph of the target software project; according to the code feature matching rule and the call graph, the correlation degree between each code snippet of the target software project and the software vulnerability is detected, and the correlation degree is used for evaluating the risk level of each code snippet of the target software project; and generating a software vulnerability analysis report of the target software project according to the association degree between each code snippet of the target software project and the software vulnerability. The technical problem that in the prior art, an open source software vulnerability detection tool cannot accurately evaluate the real influence of vulnerabilities in specific project contexts is solved.

The invention relates to a code generation method based on reasoning time extension. The method comprises the following steps: 1, in a scene without external information: step 1, generating an initial code draft with API (Application Program Interface) calling; 2, the large model considers the influence of PythonAPI version evolution on code migration according to a given rough code, library requirements and version constraints, and generates a refined code with correct API calling; and 2, introducing a rough code retrieval scene by using external information: step 1, capturing a Python library from PyPI and GitHub, and constructing an external knowledge base; the method comprises the following steps of: 1, generating a rough code snippet, 2, generating a rough code snippet, and 3, generating a refined code snippet subjected to knowledge enhancement by a large model according to a given rough code, library requirements, version constraints and retrieved knowledge snippets. The method can improve the correctness of the API, is compatible with a deployment standard, and improves the code generation capability of a large model when the large model deals with API demand evolution.

A method and an apparatus including processing circuitry are provided. The processing circuitry determines previous probability information associated with each entropy coded region of multiple regions in a previous picture. The processing circuitry selects, based on (i) a location or a quantization parameter of an independently decodable coding segment in a current picture or (ii) syntax information of the independently decodable coding segment, a region among the multiple regions. Initial probability information for one or more current syntax elements of a block in the independently decodable coding segment is determined based on the previous probability information associated with the selected region. The one or more current syntax elements are first to be entropy decoded in the independently decodable coding segment. The processing circuitry entropy decodes coded bits associated with the one or more current syntax elements into a bin string based on the initial probability information.

Apparatuses, systems, and techniques of using one or more circuits (e.g., of a network interface) to obtain assembly code for one or more machine code segments loaded and / or injected into a process, and determine whether the assembly code is likely to perform at least one unauthorized task.