281 results about "Continuous integration" patented technology

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention relates to the technical field of continuous integration and continuous delivery, and discloses a Jenkins continuous integration method based on Kubernetes. According to the method, a Jenkins server is deployed in a Kubernetes cluster to serve as a control plane, a construction environment is created through a preset multi-container Pod template, and a Pod containing a plurality of containers is dynamically created based on the Pod template, so that each container executes different construction tasks. Containers in the Pod realize collaboration through sharing a network namespace and a storage volume, and a Jenkins server monitors task execution states of the containers in the Pod and performs dynamic scheduling according to task priorities and cluster load states. And after the task is completed, automatically destroying the Pod and releasing cluster resources. According to the method and the device, dynamic expansion and contraction of a construction environment and efficient parallel execution of tasks are realized, the problem that a traditional Jenkins static resource pool is difficult to deal with load changes is solved, the resource utilization rate is improved, the expandability of the system is enhanced, and an efficient and reliable solution is provided for an enterprise-level continuous integration system.

Example solutions protect a continuous integration / continuous deployment (CI / CD) pipeline. Examples collect data from a CI / CD pipeline execution data source and / or a CI / CD pipeline task data source. Based on the collected data, a feature group comprising a plurality of records is created. Each record in the feature group represents an execution of the CI / CD pipeline. An anomaly score is generated, using a model representing historical feature groups, for the feature group representing the execution of the CI / CD pipeline. If the anomaly score is above a threshold, an alert is generated to indicate that the collected data represents an anomalous activity.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

Techniques are provided for software deployment pipeline generation using generative artificial intelligence (AI). One method comprises obtaining a trained generative AI model, trained using a plurality of continuous integration / continuous deployment (CI / CD) configuration files; obtaining information characterizing a selected software development project; and applying at least some of the information characterizing the selected software development project as one or more system prompts to the trained generative AI model, wherein the trained generative AI model predicts a portion of a software deployment pipeline associated with the selected software development project. The CI / CD configuration files used for training may be associated with an organization that is associated with the selected software development project. The predicted portion of the software deployment pipeline may comprise pipeline jobs, an automatic code completion and / or a correction of a syntax and / or a structure of at least one CI / CD configuration file being edited.

The technology disclosed herein enables identifying, in a container registry, a plurality of containers corresponding to a plurality of continuous integration (CI) components for continuous integration, deploying, to an execution environment, the plurality of containers, identifying, based on a configuration script, a dependency between a first container of the plurality of containers and a second container of the plurality of containers, and configuring, based on the dependency, the second container to access data of the first container. Each container of the plurality of containers corresponds to a CI component of the plurality of CI components.

The invention discloses a research and development test bidirectional synchronization method based on artificial intelligence, and particularly relates to the technical field of artificial intelligence and software testing. Deep semantic modeling of research and development task behaviors is realized by constructing a multi-modal causal intention map, a test coverage blind area is inferred and recognized based on a graph neural network, a completion task is reversely generated, a complete state set is formed in combination with an original task set, and a condition control vector is adopted to drive generation of a structured test case. Collecting behavior and operation logs after the test is executed, detecting a mismatch area through modal alignment, generating a repair suggestion by utilizing a deviation correction model, and automatically writing back a task system; meanwhile, a state graph dynamic updating mechanism and a reverse task scheduling engine are introduced, and linkage feedback of test results on research and development tasks is achieved; the method can improve test coverage precision and research and development cooperation efficiency, and is suitable for agile development and continuous integration scenes.

Aspects of the subject disclosure may include, for example, a method that includes receiving, by a processing system including a processor, input data relating to software application in a predetermined format, constructing a first training data set based on a set of features extracted from the input data and a set of labels associated with the set of extracted features, obtaining a classifier trained with the first training data set using supervised machine learning techniques, testing the classifier with a second training data set different from the first training data set, in response to new input data relating to the software application in the predetermined format, determining, by the classifier, an automated test, and providing the automated test to a continuous integration and continuous deployment (CI / CD) pipeline. Other embodiments are disclosed.

Techniques for processing change requests for software projects are disclosed. An example method includes receiving a change request describing new source code for a software project. The method also includes generating, by a processing device, a prompt comprising context information and a plugin, wherein the context information comprises source code from the software project and the plugin comprises text that describes a supplemental work product to be generated. The method also includes providing the prompt to a machine learning model, wherein the context information trains the machine learning model to provide the supplemental work product with a correct content and format. The method also includes receiving the supplemental work product from the machine learning model. The method also includes applying the supplemental work product to the software project.

The invention discloses an application program deployment method and device and a storage medium, and the method comprises the steps: automatically generating a mirror image file of an application program through a continuous integration tool, and automatically pushing the mirror image file to a container mirror image warehouse; and automatically pulling the latest mirror image file from the container mirror image warehouse to a plurality of target servers through a configuration file or a container scheduling tool, and starting container instances on the plurality of target servers in batches, thereby deploying the application programs on the plurality of target servers in a large scale. According to the method, automatic compiling, mirror image generation and containerization deployment of project codes are realized, so that the problem of low automation level in application program deployment in the prior art is effectively solved, and reliable guarantee is provided for stability and high efficiency of a production environment. The method can be widely applied to the technical field of computer operation and maintenance.

The invention relates to the technical field of software engineering, and discloses an assembly line flow adaptive configuration method and system. The method comprises the following steps: obtaining source code data of a software project and performing static analysis and feature extraction to generate structured language description data; querying and matching in a preset template library based on the structured language description data to generate a task template set; performing task dependency relationship analysis and process topology assembly on the task template set to generate initial pipeline data; and performing dynamic strategy adjustment on the execution process of the initial pipeline data by adopting the real-time context data corresponding to the software project to generate target pipeline data. Through automatic language recognition and intelligent process generation, automatic configuration of the continuous integration process is realized, and the technical problem of low automation level caused by lack of automatic recognition and adaptation capability for a project development language environment in a project continuous integration and continuous deployment technology is solved.

The invention relates to the technical field of mobile application continuous integration automation, and discloses a multi-channel APP automatic packaging system which comprises the steps that byte code files in a released mobile terminal APP installation package are acquired, and an occupied symbol space set is generated; generating an available confusion namespace by using set complementary operation; extracting method-level code change by using an abstract syntax tree difference algorithm, and generating a change method set; generating a patch special confusion rule for the change method set by using a namespace allocation algorithm; compiling and obfuscating the change method set by applying a patch special obfuscating rule to generate a safe obfuscating patch byte code; performing packaging and signature operation on the safe obfuscation patch byte code, and outputting a minimized obfuscation safe hot repair patch pack; according to the method, the technical problems that class definition conflicts or method calling errors occur after the hot repair patch is loaded, and the downloading success rate of a user is affected due to the overlarge size of the hot repair patch are solved.

The disclosed system and process involve correlating outbound traffic with a CI / CD pipeline. CI / CD Pipelines consist of multiple jobs, and each job's steps execute on the same runner host. Network monitors relay information about domain names and network connections during job execution to the data store. Once all pipeline jobs are complete, the correlation server links domain names and connections to determine endpoints. This correlation, showing outbound traffic for each step, is accessible to the pipeline owner for review. An alert is triggered for new endpoints relative to the baseline. Owners can block unexpected endpoints based on explicitly defined lists. The correlation server also identifies file events, aiding in detecting suspicious file and software build overwrite events for combating software supply chain attacks. Observed file and network events contribute to generating a Software Bill of Materials (SBOM) through runtime events.

The invention belongs to the technical field of automatic testing, and particularly relates to a web automatic testing method and system.The method comprises the steps of automatic script recording and dynamic adaptation, layered framework collaborative testing and data verification and continuous integration testing and test report generation. Wherein automatic script recording is dynamically adapted: recording a page operation script through a playwright tool and applying the page operation script to a UI layer, and when the page UI changes, recording the script again and replacing the original script without element-by-element positioning and code modification; in the hierarchical framework collaborative testing and data verification, a data access layer inputs test data, a UI layer executes page operation and captures display data, and a business logic layer calls an API interface to obtain real data and compares the real data with the display data for verification. According to the method, a web automatic test framework is constructed in combination with the advantages of playwright, a structured engineering project is more suitable for project operation and maintenance, and a sustainable integrated test environment is constructed by utilizing jenkins, so that test input, execution and output are automated.

A continuous integration (CI) pipeline can be executed based on a type of content for a change to source code. For example, a computing system can receive the change to the source code of a software application in response to a request for changing the source code being generated. The computing system can identify the type of content included in the change to the source code. Specifically, the computing system can determine whether the type of content is associated with a prevention of an execution of the CI pipeline. In response, the computing system can perform an action associated with the change to the source code prior to a deployment of the change to the source code.

The invention discloses an automatic testing method and device based on a front-end UI and a terminal, and belongs to the technical field of front-end development testing. Comprising the following steps: detecting a first test instruction of each set of front-end UI program, controlling to carry out demand analysis, determining a function module which needs to be subjected to UI automatic test according to a demand analysis result, and determining a test target; performing automatic technology type selection, and selecting a specified test tool and a continuous integration tool; generating a compiled test case according to the determined function module needing to be tested and the test target; running the test case, and generating a test script according to the test case and an automation technology type selection result; when the test script is verified to be correct, integrating the test script into a continuous integration tool, and automatically running a test through the test script when the code of the subsequent version of the front-end UI program is submitted; and generating a test report, positioning problems and giving repair suggestions. According to the invention, the testing efficiency is improved, and the labor cost of testing is reduced.

A computing platform may configure a dependency knowledge graph indicating file dependencies for mainframe applications, and an error knowledge graph indicating errors and corresponding solutions for the mainframe applications. The computing platform may receive mainframe source code. The computing platform may analyze, using the knowledge graphs, the mainframe source code to identify potential errors and corresponding solutions. Based on identifying an error in the mainframe source code, the computing platform may cause the mainframe source code to be updated according to the corresponding solution. The computing platform may analyze, using the dependency knowledge graph and the error knowledge graph, the updated mainframe source code to identify remaining errors. Based on identifying an absence of the remaining errors, the computing platform may send, to a mainframe build and deployment engine, the updated mainframe source code, which may cause the mainframe build and deployment engine to automatically execute a build process.

A data processing system implements after determining that a test in a target branch of a code repository starts failing, receiving a pull request (PR) onto the target branch and blocking the PR (invoking a build policy and a test policy); after determining that the failing test in the target branch is disabled, tracking the disabled test in a disabled test file; in response to a request to requeue the test policy, determining whether artifact in the build policy is identical with artifact in the test policy; when not identical, fetching all versions of the file within a time period, generating a union content of all versions of the file, and applying the requeued test policy using the union content, the time period being between a start time of the build policy and a start time of the requeued test policy; and unblocking the PR.

Systems, devices, and methods related to automated management of deployment of applications are provided. An example computer system includes one or more processors and a computer-readable storage media storing computer-executable instructions. The instructions when executed by the one or more processors, cause the computer system to receive a request for deploying an application in a production environment, access data generated during continued integration / continued development (CI / CD) pipeline execution of the application, analyze the data to determine a status of the application, determine whether the application is ready for deployment based at least in part on the status of the application and a predetermined policy, and block the application from deployment in response to a determination that the application is not ready.

The invention provides a vulnerability detection method and system based on incremental codes, and belongs to the technical field of software security detection. Aiming at the technical problems that the software vulnerability detection in the prior art needs to carry out full scanning on codes, the detection efficiency is low and the method is difficult to adapt to rapid iterative development, whether patch codes can trigger potential software vulnerabilities or not is identified by analyzing the influence of code patches on the behavior logic of original codes. A code attribute graph is constructed by adopting a static code analysis technology, and changes of a data flow and a control flow are analyzed; comparing and analyzing the addition and deletion codes in the patches before and after; and an extensible detection framework is designed, and flexible configuration of different types of vulnerability detection rules is supported. According to the method, repeated scanning of full-quantity codes is avoided, and the vulnerability detection efficiency is remarkably improved; the security risk introduced by the patch is accurately positioned through an incremental analysis method; the adoption of an extensible architecture facilitates continuous integration of new detection capabilities.

A method in one embodiment comprises implementing a first process for carrying out an application deployment configuration change using a deployment repository, and implementing a second process for carrying out an application logic change using a source repository, the second process being different than the first process. The method further comprises identifying a particular type of change to be made to at least one application, and controlling execution of a particular sequence of one or more instances of at least one of the first process and the second process responsive to identification of the particular type of change to be made to the at least one application. The deployment repository and the source repository are illustratively part of a continuous integration / continuous deployment (CI / CD) system. The CI / CD system controls software code for applications executed by host devices of a host platform coupled to the CI / CD system over at least one network.

The invention provides a large language model modal expansion method and device based on parameter fusion and decoupling, and relates to the technical field of large language models. The method comprises the following steps: performing fine adjustment on a pre-training language model to obtain a plurality of multi-modal large language models; task vector extraction is carried out on each multi-modal large language model; sparsification is carried out on the original task vector by adopting a sparsification strategy to obtain sparse vectors, and fusion is carried out on the sparse vectors to obtain a fusion task vector; constructing model parameters according to the fusion task vector; constructing a mode-exclusive binary mask for each multi-mode large language model according to the fusion task vector; and constructing a fusion model according to the model parameters and the binary mask. The invention provides a multi-modal language model extension method with non-training fusion, modal decoupling, performance retention and continuous extension capabilities, which is suitable for efficiently integrating a plurality of MLLMs, reconstructing an original model structure, coping with application scenes such as continuous integration of new tasks and the like.

The invention relates to the technical field of ship manufacturing, and discloses a general assembly construction data service encapsulation and construction method based on a cloud native technology. The method comprises the following steps: dividing a ship final assembly construction data source into design, production, quality inspection, logistics and operation and maintenance data units, and determining a data type and an interface protocol; then, constructing an initial service model based on a micro-service architecture and a containerization technology; then, dynamic service packaging is carried out, and cross-model data routing and load balancing are achieved; dynamically allocating resources and scheduling tasks through a multi-target scheduling algorithm model; iteratively optimizing the service model by utilizing an incremental learning mechanism; and finally, realizing gray release and rolling update by continuously integrating and continuously delivering assembly line deployment to a cloud native platform. The ship general assembly construction data processing efficiency, the system performance and the service quality are improved, and the problems of disordered data management, low processing efficiency, unreasonable resource allocation and the like in a traditional mode are effectively solved.

The invention provides an automatic software testing method combining natural language processing (NLP) and natural graphics processing (NGP). According to the method, a demand document is analyzed through NLP, key functions are extracted, test case description is generated, meanwhile, a software user interface is recognized and analyzed through NGP, interface elements and interaction logic are extracted, and an interface model is constructed. And automatically generating a test case and a script based on a demand analysis and interface modeling result, executing, recording and analyzing a test result through an automatic test framework, identifying defects, and proposing optimization suggestions. Experiments show that the method can significantly improve the test coverage rate and efficiency, and is suitable for automatic testing and complex interface interaction verification in a continuous integration environment. Through combination of NLP and NGP, intelligence and automation of software testing are realized, and the method has a wide application prospect.

An agent deployment request is received for an external computing environment. An agentic module is configured for deployment to the external computing environment based on characteristics of the external computing environment, wherein the agentic module comprises a computational agent operable to process information stored within the external computing environment and a corresponding agentic controller. Responsive to the agent deployment request, a first unit of software instructions is generated that, when executed, instantiates an agentic module instance within the external computing environment. The first unit of software instructions is transmitted to the external computing environment. A performance metric related to processing of the information stored within the external computing environment by a computational agent instance of the agentic module instance is received from an agentic controller instance of the agentic module instance.

The invention discloses an increment testing method and device, a storage medium and electronic equipment, and the method comprises the steps: constructing a dependency graph for describing the dependency relationship between software components in a software system according to software system data, and carrying out the continuous integration of the software components on the basis of a code change set corresponding to a current continuous integration task; a minimum candidate influence range is determined on the dependency graph, a candidate test set is delineated, tests in the candidate test set are ranked according to the sequence of risk scores of all the tests from high to low, a target candidate test set is obtained, all the tests are processed through a greedy approximation algorithm, and a target candidate test set is selected under the constraint of resource budget; and the minimum and optimal test set of software system defects is found to the maximum extent. According to the method, the minimum candidate influence range is determined on the dependency graph based on the code change set, the greedy approximation algorithm is combined, intelligent and accurate selection of the test set under the resource budget constraint is achieved, it can be guaranteed that the defect detection rate is not reduced, and the continuous integration process efficiency can be improved to the maximum extent.

A method includes identifying a pipeline of a plurality of pipelines of a continuous integration and continuous delivery / deployment (CI / CD) system, selecting a repository of a plurality of repositories utilized by the identified pipeline, modifying the repository, determining whether to test the modified repository and responsive to determining to test the modified repository, executing the identified pipeline with the modified repository.

The invention discloses a large model automatic deployment method and device, equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: detecting current available GPU resources of a target server, determining deployment estimation resources needed by a deployment model through a preset continuous integration tool according to preset model parameters, establishing a GPU index for calling available GPU resources according to the deployment pre-estimation resources; modifying the preset model loading script according to the preset model parameters and the GPU index to obtain a target model loading script; and executing the target model loading script to deploy the target model in the target server based on the model data stored in the specified directory, and performing a deployment completion degree test on the target model after the target model is deployed. Therefore, the deployment efficiency and reliability can be improved by realizing the automatic construction, deployment and test process of the large model, and meanwhile, the complexity and error rate of manual operation are reduced.

Various methods and processes, apparatuses or systems, and media for automating development, testing, and productionizing a pipeline for users are disclosed. A processor receives a request from a user to access an application, the request including user's credentials data; grants access to the application based on verifying the user's credentials data with prestored credentials data received by calling an authentication server; identifies the user's role within a computing environment; automatically presents a template that corresponds to the user's role allowing the user to write code to source data either by bringing the user's own data into the computing environment or by connecting to data that resides in a database; automatically integrates the written code with a continuous integration continuous delivery pipeline for production of a model; and deploys the model after training and testing the model while managing and maintaining all necessary guardrails from a control standpoint within the computing environment.