4012 results about "Source code" patented technology

A system and method is described for internationalization of web pages by extracting translatable content from extensible mark-up language (XML), or similar data-centric meta-language representations of web pages or data used to build web pages. The extracted translatable content is stored in a translation task repository (TTR) accessible by the web developer and the translator. The XML representation is then modified to include selection control logic to select the appropriate translations for insertion into the final web page. The translator accesses the TTR to translate the appropriate content and saves the translations back to the TTR associated with the original translatable data. The translations are obtained from the TTR as selection cases for the selection control logic of the XML representation. As the XML is converted into the web source code, the selection logic and translations are embedded therein facilitating building the web site in multiple different languages.

An example operation may include one or more of receiving, via a user interface, a request to test a software program, reading source code of the software program and identifying a vulnerability in the source code based on the reading, generating repair code for fixing the identified vulnerability based on execution of a generative artificial intelligence (GenAI) model on the source code and a repository of repair code used to repair previous vulnerabilities, and displaying information about the repair code via the user interface.

An example operation may include one or more of executing tests on a software application via a test environment of a test platform and logging results of the tests in a log file, identifying a new feature to be added to the software application based on execution of a machine learning model on logged results of the tests stored in the log file, generating source code for the new feature to be added to the software application based on execution of a generative artificial intelligence (GenAI) model on the new feature and a repository of source code, and displaying the generated source code via a user interface of the software application.

An example operation may include one or more of monitoring communications that occur with user devices over a shared computer network, detecting a security threat from the monitored communications, generating a software program to simulate the security threat over the shared computer network based on execution of a generative artificial intelligence (GenAI) model on a description of the security threat and a repository of source code, installing the source code for simulating the security threat on a system associated with the computer network, and executing the source code for simulating the security threat via the system.

An example operation may include one or more of receiving a first code base and a second code base from a software repository, wherein each of the first and second code bases comprise source code of a plurality of different software programs, identifying a source code within the first code base that is interdependent with a source code within the second code base based on execution of a generative artificial intelligence (GenAI) model on the first and second code bases, determining a software component to connect the source code within the first code base to the source code within the second code base, and displaying an identifier of the software component via a user interface.

The invention relates to the technical field of software engineering, in particular to a source code detection method, which comprises the steps of source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior simulation, intelligent vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A support vector machine and a K-means clustering algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a continuous optimization feedback mechanism is introduced, a mixed vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures, vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively improved.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention provides a security data penetration test method. The method comprises the steps of pre-establishing a multi-thread application program race condition vulnerability feature library, performing static analysis on source codes of a target application program, identifying potential race condition vulnerability points, and obtaining a preliminary vulnerability candidate set; for monitored suspected race condition events, abstracting event context information into constraint conditions, exploring an input combination of suspected static conditions by using a constraint solver, and determining a minimum input set of stable triggering race conditions; aiming at memory models and synchronous primitives of different programming languages, obtaining a race condition inverse mode, and extracting universal utilization primitives covering various race condition vulnerabilities; in the penetration test process, static analysis, dynamic monitoring and symbolic execution are applied to mine race condition vulnerabilities of a target application system, the availability of the vulnerabilities is verified, and evaluation of system security is assisted to be completed.

A neural classifier model is used to detect cybersecurity vulnerabilities in the source code predicted by a deep learning code generation model having been trained on source code possibly containing security bugs. Upon the classifier model classifying a given source code snippet as likely containing a cybersecurity vulnerability, a proposed repair for the cybersecurity vulnerability is predicted from a neural decoder transformer model having been trained on non-vulnerable source code. The neural decoder transformer model is used to predict source code that repairs the cybersecurity vulnerability given the source code classified with a cybersecurity vulnerability.

The embodiment of the invention discloses a causal chain-based front-end state anomaly detection method and system. The method comprises the following steps: S1, identifying a calling relation among an event monitor, an asynchronous task and a state change logic by analyzing a source code, and constructing an expected causal chain model; s2, dynamically verifying whether the actually occurring causal chain conforms to an expected model or not during the application running period; wherein in the step S1, the analysis of the source code comprises but is not limited to an AST technology; in the step S2, the hijacking mode of the key function comprises but is not limited to a Proxy mechanism, function packaging and a Hook technology. According to the method, the expected causal chain model is constructed, the actual path is dynamically verified during operation, system inconsistency caused by causal chain abnormity is avoided, the AI model is utilized to generate the repair strategy suggestions, a traditional low-efficiency troubleshooting process depending on logs and breakpoint debugging is upgraded to intelligent decision, and the repair efficiency is remarkably improved.

The invention relates to the technical field of software reliability guarantee, in particular to a code vulnerability detection method based on efficient parameter fine tuning of a code pre-training model, which comprises the following steps: preprocessing source codes of a vulnerability data set; s2, converting the preprocessed source code, and extracting a token sequence and a code attribute graph path corresponding to the source code; s3, constructing a code vulnerability detection model, wherein the code vulnerability detection model comprises a semantic feature extractor, a structural feature extractor, a feature fusion layer and a classifier; s4, jointly training a code vulnerability detection model by using the token sequence corresponding to the extracted source code and the code attribute graph path; and S5, through the optimized code vulnerability detection model, outputting a result that whether the source code to be detected has vulnerabilities or not. According to the method, the model is finely adjusted through efficient parameter fine adjustment, training resources are saved, good performance is obtained, and the code vulnerability detection efficiency and accuracy are improved by fusing and learning semantic and structural features of source codes at the same time.

The invention discloses a test case determination method and device, a computer readable storage medium and a processor. The method comprises the following steps: identifying a source code of software by utilizing a pre-trained large model to obtain a plurality of functional modules in the software and a dependency relationship among the plurality of functional modules; in response to updating of the source code of the software, determining a code updating part in the source code; determining a test case matched with the code updating part from a test case library of the software based on the code updating part and a dependency relationship among the plurality of functional modules, the test case being used for testing the code updating part; based on a test result of the test case, adjusting a selection strategy of the test case; and re-determining the test case for testing the code updating part based on the adjusted selection strategy of the test case. The technical problem that a traditional regression test strategy is low in test efficiency is solved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects. In one aspect, a method includes obtaining data indicative of initial project plan parameters associated with a software development project; identifying multiple program modules associated with the software development project; obtaining, for a first set of modules of the multiple modules, corresponding source code segments; executing a quality assessment process to compute, for each corresponding source code segment, a corresponding quality score; identifying, based on the quality scores, that at least a subset of the corresponding source code segments are integrable into a corresponding first subset of the first set of modules; and generating a revised project plan by generating updates to the initial project plan parameters by accounting for the corresponding source code segments being integrated in to corresponding ones of the first set of modules.

Systems and methods for detecting vulnerabilities in software in real time. In some aspects, the system receives a training dataset, including first source code, first runtime data flow, a set of sample vulnerable libraries, and a set of sample replacement software libraries. Using the training dataset, the system trains a vulnerability detection model. The system receives second source code being tested in a development environment and collects the second source code and second runtime data flow. The system processes the second source code and second runtime data flow using the vulnerability detection model to identify novel vulnerable software libraries and / or known vulnerable software libraries. The system generates suggestions to replace the known and / or novel vulnerable software libraries. In response to detecting a user choosing a first replacement library in place of a first vulnerable library, the system replaces the first vulnerable library with the first replacement library.

The invention discloses an intelligent risk identification and self-adaptive repair method, system and device for a software supply chain and a medium, belongs to the field of network security and automatic software engineering, and aims to solve the technical problem of how to accurately and comprehensively identify software code supply chain risks including code snippets. A reliable and efficient automatic closed-loop repair scheme is provided, and the technical defects that in the prior art, the software code supply chain recognition range is limited, the repair process is rigid and the reliability is low are overcome. Analyzing the declarative dependency; meanwhile, semantic traceability based on artificial intelligence is carried out on the code snippets, and a global software material list is generated; and performing intelligent mapping on the software components in the global software bill of materials and the vulnerability database to identify risks.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

A computer-implemented method is disclosed. The method includes: receiving a request for retrieval of data satisfying one or more criteria, the request including at least one data request parameter; searching a database storing example queries based on the request to identify at least one matching query; providing, to a large language model (LLM), an input prompt to generate a query purporting to retrieve data satisfying the one or more criteria, the input prompt including the at least one data request parameter and the at least one matching query as an example; and receiving, from the LLM, a result including the generated query.

The invention provides an SCA and SAST combined vulnerability reachable path detection method and device. The method comprises the steps of obtaining a to-be-detected item; extracting an entry function based on the to-be-detected item; when the to-be-detected item is a source code item or a product item, generating a function call graph based on call graph generation logic corresponding to the source code item or call graph generation logic corresponding to the product item; identifying a vulnerability function in the dependent component of the item to be detected through a software component analysis tool based on preset software bill of material information; taking the vulnerability function as a starting point, performing depth-first search traversal in the function call graph, and detecting whether an entry function is traversed or not; and if yes, determining a path from the entry function to the vulnerability function as a vulnerability reachable path. According to the method and the device, the reachable path of the vulnerability can be automatically detected, so that whether the security vulnerability is triggered or not is determined, invalid checking of the SCA tool in the use process can be avoided, and the reliability of the SCA tool is improved.

A method is provided for optimizing regression testing in a software development environment. The method includes analyzing source code to create a structural dependency graph that maps dependencies between code elements; identifying changes in the source code between a current version and a previous version; mapping the identified changes onto the structural dependency graph to determine affected code elements; selecting a subset of regression tests based on the affected code elements identified in the dependency graph; and executing the selected subset of regression tests to validate the changes in the source code.

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

Disclosed herein are system, method and / or computer program product embodiments, and / or combinations and sub-combinations thereof for providing a platform for developing an application that interacts with a set of IoT devices. An embodiment provides a source code editor that enables a user to generate source code of the application and an application builder that generates an executable version of the application based at least on the generated source code. Generating the source code includes defining an input or output of the application as an event that is mappable to a first set of IoT devices in a first network of IoT devices when the application is installed thereto and to a second set of IoT devices in a second network of IoT devices when the application is installed thereto, the first set of IoT devices including at least one device type not included in the second set of IoT devices.

The invention provides a cross-platform low-code application development system based on a Flutter framework. Comprising a login registration module, a project management module, an interface design module, a state management module, a route management module, a team management module, a help document module, an AI page design module, a development document generation module, a hardware integration module, a code generation module, a multi-user collaborative development module, a communication module, an online simulator module, an AI generation application module and an interface management module. An asset management module, a developer forum module, an application market module and an application packaging module. The communication module supports various communication protocols and secure transmission modes, and ensures that data exchange applied to cross-platform and cross-system environments is secure and reliable. Through component dragging and graphical configuration, the low-code platform automatically analyzes configuration data to generate an App, a development document and a source code. And meanwhile, multi-person collaborative development is supported, so that the development threshold is reduced and the development speed is increased.

The invention provides an automatic incremental compiling method and system for Java development, and the method comprises the steps: carrying out the hash value verification of an obtained Java source code, and obtaining the change event information in the Java source code; extracting static dependency information and a dynamic calling relation from the change event information, and generating a relation dependency graph according to the static dependency information and the dynamic calling relation; based on the relation dependency graph, generating a compiling task queue by utilizing a topological sorting algorithm; compiling the change event information in the Java source code according to the compiling task queue to obtain a machine code corresponding to the change event information; according to the method, code change information is accurately positioned through hash value verification, so that the initial range of incremental compiling is favorably reduced; according to the method, the static dependency information and the dynamic calling relation are extracted from the change event information, the relation dependency graph is generated, the dependency relation between the code units is completely represented, the problem that the dynamic calling relation is omitted is solved, and compiling correctness and efficiency are improved.

The invention provides a large-model-assisted program function automatic perception fuzzy test method and system, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a source code and a description document of a to-be-tested program; analyzing the source code and the description document based on the first large model to obtain all functional parameters supported by the to-be-tested program; identifying a dependency relationship and a constraint condition among the functional parameters based on the source code and the extracted functional parameters; executing a fuzzy test based on the dependency relationship between the function parameters and the constraint condition; and automatically analyzing the test result of the fuzzy test based on the second large model to generate a structured report. According to the method, the coverage rate and effectiveness of fuzzy testing can be optimized, and the quality and safety of a complex software system are comprehensively guaranteed.

The invention discloses a log quality detection method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a multi-modal input source; performing reverse anchoring operation in a dynamic analysis stage of the running log to establish direct mapping between the running log and the source code; compiling the abstract syntax tree based on the source code, establishing a grammar dependence graph according to the abstract syntax tree, and traversing the abstract syntax tree to construct a structured data set; based on the running log, obtaining a quality detection result through structured analysis, and generating a semantic detection result by using a large language model; performing cooperative operation on the structured data set and the grammar dependency graph to generate a code log coverage score, and performing arrangement according to a quality detection result and a semantic detection result to obtain an abnormal mode feature; and performing feedback adjustment based on the code log coverage score and the abnormal mode feature in combination with a preset correction rule. The method can efficiently and accurately realize log quality detection, and can be widely applied to the technical field of data processing.

The invention relates to the technical field of vulnerability detection, and provides a fine-grained vulnerability detection method and system based on semantic perception sparse attention, and the method comprises the steps: analyzing a to-be-detected source code, and generating an abstract syntax tree, a control flow graph and a program dependency graph; carrying out weighted fusion on graph representations of the abstract syntax tree, the control flow graph and the program dependency graph, and integrating into a code attribute graph; for the code attribute graph, processing the code attribute graph by adopting a semantic perception sparse attention method combined with sparse attention, a learnable Token pruning method and Top-k interaction frequency to obtain a Token attention score; and based on the obtained Token attention scores, accumulating the attention score of each Token in each row of the code, and detecting and determining a statement where the vulnerability code is located. According to the detection method disclosed by the invention, the capability of modeling code semantic information and capturing a structured dependency relationship can be effectively improved, and meanwhile, the calculation complexity is remarkably reduced.

The invention provides a Linux access control system based on attributes, and relates to the technical field of data access control. The system comprises a system monitor module, a data interaction module and a decision unit. The system monitor module collects attributes from a kernel and a user space and writes the attributes into the data interaction module; the access decision unit compiles the access control strategy into an eBPF program and mounts the eBPF program to a corresponding hook; executing the kernel to the hook, and triggering an eBPF program; an eBPF program queries a Flow rule; matching the attribute with the Flow rule, and if the matching is successful, executing a corresponding action; if all the Flow rules fail to match, executing a default action; the system can be expanded during operation, and can be loaded or unloaded based on dynamic loading characteristics and strategies of the eBPF program and the eBPF program during operation of the system, so that the problem that a kernel needs to be compiled in a traditional LSM scheme is solved; the method does not intrude the kernel, is completely based on an eBPF program, does not modify a kernel source code, and can guarantee the stability and compatibility.

The invention discloses a PHP taint type vulnerability detection method based on a heterogeneous graph neural network, and belongs to the field of software security. The method comprises the following steps: performing annotation removal, variable naming standardization and character string standardization processing on a PHP source code through a code preprocessing module to generate a standardized code; based on a vulnerability sub-attribute graph extraction module, reversely tracking vulnerability sinks to a taint source, extracting a simplified vulnerability sub-attribute graph, and removing redundant nodes and edges; fusing BERT semantic features and node type features through a graph node embedding module to generate an initial embedding vector, and constructing a heterogeneous graph comprising an abstract syntax tree edge, a program flow graph edge and a control dependence graph edge; a heterogeneous graph neural network vulnerability detection module is adopted to perform independent feature aggregation on multiple types of edges, dynamic weighted fusion is performed in combination with an attention mechanism, and key nodes are screened through Top-k graph pooling; and finally, inputting the graph-level features into a classifier to realize vulnerability detection.

The invention provides a distributed storage method based on source code semantic partitioning, and particularly relates to the technical field of cloud data distributed storage. The method comprises the steps of performing semantic partitioning on a source code, and segmenting the source code into a plurality of semantic blocks according to dimensions such as functional semantics, an abstract syntax tree structure, author information and version information; generating metadata containing information such as grammar type tags, file paths, line number ranges, author identifiers, version identifiers and access popularity for each semantic block; constructing a weighted directed acyclic graph (DAG) based on the semantic chunks and the dependency relationship thereof; superposing a metadata layer in the DAG structure, and recording information such as function call dependency, inter-block reference relationship and version evolution chain; blocks with relatively high access frequency and close semantics are aggregated into super blocks, the traversal depth is reduced, and meanwhile, hot data and cold data are differentiated for hierarchical storage by adopting a cold and hot data management strategy; and evaluating a parent block aggregation degree through a BDS algorithm, determining a block sorting priority, and optimizing super block boundary division. Compared with the prior art, the method has the advantages that the semantic retrieval efficiency, the incremental updating capability and the distributed query performance of the source code storage system are improved.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.