644 results about "Code segment" patented technology

Certain aspects of the disclosure are directed to searching programming code repositories. In examples, a method is provided that includes receiving a natural language search query via a graphical user interface; converting the natural language search query into a vector representation; determining a proximity score between the vector representation of the natural language search query and at least one vector representation corresponding to a machine learning model generated natural language description of a programming code segment; and providing a search result corresponding to the programming code segment based on the proximity score.

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.

Provided in the present application is a code generation method, comprising: a code development platform receives input information by a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information so as to obtain target business knowledge; then the code development platform combines the input information, the context information and the target business knowledge according to a prompt template, so as to obtain prompt information; and then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and presents to the user a code segment reasoned by the LLM. Thus, using the prompt information from the same data source in different processes for prompting the LLM can achieve complete sharing and complete alignment of the prompt information, thereby improving the accuracy of generating code in single time. Furthermore, using a unified prompt template to combine the context information and the business knowledge can make a code segment generated by the LLM more accurate, thereby improving the code acceptance rate in professional domains.

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 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 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.

Embodiments of the present invention provide a code reconstruction method and system, a computing device, a storage medium and a program product wherein the code reconstruction method comprises: performing code analysis on a to-be-reconstructed initial code to generate a target reconstruction scheme wherein the target reconstruction scheme comprises a code reconstruction plan and a code test plan; the code reconstruction plan comprises a function dependency relationship of the target code; analyzing a function dependency relationship in the code reconstruction plan, and generating at least two code reconstruction tasks corresponding to the initial code; executing the at least two code reconstruction tasks in sequence to obtain each initial code snippet, and iteratively optimizing each initial code snippet through static analysis and dynamic verification until candidate code snippets conforming to a function dependency relationship are generated; and compiling a test case of each candidate code snippet based on the code test plan, and testing each candidate code snippet based on each test case to obtain a plurality of target code snippets conforming to the function dependency relationship. And the coherence and the consistency of the reconstructed codes are ensured.

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.

Systems are provided for generating, modifying and using SBOMs for facilitating risk assessment and threat mitigation for corresponding programs, and particularly for large programming builds. The creation and modification of the SBOMs includes processes for omitting declarations referenced in chunk SBOMs of program chunks incorporated into a final programming build associated with a build SBOM, but which are not actually utilized by the final programming build, as well as processes for adding new declarations for code segments that are not declared in the related chunk SBOMs, even though the code segments are utilized by the final programming build. Systems are also configured to use SBOMs in combination with configuration restriction records to assess and resolve threat events in a manner that can prevent unnecessary remedial actions for threat events that appear to be relevant to one or more files or dependencies incorporated into a program.

The LLM-based code completion method comprises the following steps: splitting a code file stored in a code warehouse into a plurality of code snippets, and calculating relevancy scores, corresponding to the code snippets, of a to-be-completed target code file; the relevance score is used for representing the relevance degree of the code snippets and the target code file; at least one target code snippet with the highest relevancy score is screened out from the multiple code snippets, the at least one target code snippet serves as a query index, and a context code set used for conducting code completion on the target code file is retrieved from a knowledge base; wherein the knowledge base is constructed on the basis of code files stored in a code warehouse; and inputting the retrieved context code set and the target code file into a first LLM used for code completion, so that the first LLM performs reasoning calculation on the target code file based on the context code set, and a completion code corresponding to the target code file is generated.

The invention provides a retrieval enhancement code generation method and system based on retrieval query statements. The method comprises the following steps: acquiring a plurality of retrieval query statements; screening out context information matched with the natural language requirement and the retrieval query statement from the code library; generating a first code segment according to the natural language requirement and the context information; through a plurality of first code segments in one-to-one correspondence with the plurality of retrieval query statements and a corresponding label of each first code segment, training and optimizing a retrieval query statement generator to obtain a target retrieval query statement generator; based on a target natural language requirement, utilizing a trained and optimized target retrieval query statement generator to generate a query statement; and using the query statement to retrieve the related code context, and inputting the target natural language demand and the retrieved code context into the code generation model to complete the code generation of retrieval enhancement. According to the embodiment, the interference of redundancy and irrelevant content can be effectively reduced, so that the code generation quality is improved.

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 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 is suitable for the technical field of information security, and provides an Android APP multi-tamper-proofing method, which comprises the following steps: acquiring a dex file of an APP, and encrypting the dex file by adopting a reinforcement algorithm to generate a dex reinforced file; a signature obfuscation algorithm is adopted, obfuscation code segments are embedded in the dex reinforcement file, and a signature verification module is generated; carrying out encryption processing on the resource file of the APP by adopting an encryption storage algorithm to generate a resource encryption file; when the APP is started, secondary integrity detection is carried out on the APP based on the signature verification module; if the integrity of the APP is not destroyed, loading the resource encryption file, and carrying out decryption operation on the resource encryption file; and after the decryption operation, the key business logic code segment is verified in the APP operation process through the operation verification method, the tampering and attack resisting capability of the APP is effectively improved, and therefore the safety and integrity of the APP are protected.

A method for real-time evaluating code leakage during software code development when the developer is using a code assistant tool, comprising performing code leakage estimation by identifying and processing, using an LLM-based model, the most updated code segments as the segments evolve; and evaluating, using the LLM-based model, the extent to which a written code has been inadvertently revealed to one or more code assistant servers by reconstructing the original code from the requests sent to each code assistant server. A method for real-time mitigating code leakage during software code development process when the developer is using a code assistant tool, comprising mitigating code leakage during code writing process by manipulating, using an RL agent, prompts and data being sent to a code assistant service provided by one or more code assistant servers.

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 a method and device for encoding a BIM model based on a mapping rule, a medium and equipment, and the method comprises the steps: determining an encoding structure and presetting a fixed code segment according to an actual project demand, and completing the formulation of an encoding rule; performing code segment data summarization on all systems and subsystems related to an engineering project, and constructing a coding directory tree according to a coding rule; establishing a mapping rule between branches and leaves of the coding directory and the BIM model; opening a BIM file based on BIM platform software to obtain corresponding BIM model attribute information, and importing the BIM model attribute information into the coding directory tree; performing automatic coding on all BIM models in the current BIM file according to the coding directory tree data; and obtaining change information, and processing the code updating operation after the change occurs. According to the method, a complex and accurate mapping relation is established by using the coding directory tree, and the corresponding BIM model is automatically searched, so that the problems that manual operation is complicated, time-consuming, labor-consuming, error-prone and the like are avoided, and the coding efficiency is improved.

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.