353 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.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

A method for developing and deploying a machine learning pipeline is provided. The method may be implemented by at least one processor. The method may include collecting different types of data from one or more data sources, wherein the different types of data includes batch data and real-time data; continuously processing the data to build a plurality of layers of a Lambda application, wherein each layer is configured to be deployed independently of the other layers; building a modular machine learning pipeline in a modifiable reference file, wherein the modular machine learning pipeline defines a framework comprising a no-code / low-code parameterized machine learning framework; deploying the modular machine learning pipeline as a single deployment machine learning pipeline that can be modified for different use cases by revising the source code of a CI / CD (Continuous Integration and Continuous Delivery / Continuous Deployment) pipeline, while the framework of the machine learning pipeline remains unchanged.

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.

In an example embodiment, a root cause of a CI / CD pipeline is identified automatically from event logs of the CI / CD pipeline. A solution is then suggested automatically using an artificial intelligence analysis. More particularly, the identified root cause (e.g., error) and contextual information about the system and / or application being examined may be passed to an AI engine to predict one or more solutions to the root case.

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.

The invention discloses a database driven document content pressure testing and monitoring system and method. The system comprises a database query module, a data processing module, an automatic test triggering module, a pressure test execution module, a real-time monitoring module, a data analysis and feedback module and a configuration and management module. According to the method, the JMeter is integrated into the CI / CD tool, the JMeter is configured to interact with the common CI / CD tool so as to support automatic triggering and execution of testing, continuous integration process design is adopted, check points are set in the CI / CD process, after code changes and is pushed to a version control system, a bound JMeter performance testing task is automatically started, a real-time monitoring and feedback mechanism is adopted, and the performance testing efficiency is improved. The test task comprises a component for monitoring performance indexes in real time, so that data can be captured in real time during the test execution period, and feedback can be immediately provided for the development team after the test is completed.

The invention discloses a regression testing method and device, equipment and a medium, and relates to the technical field of software testing, and the method comprises the steps: collecting current code change data between a previous version code and a current version code of to-be-tested software, and generating current change details; performing static code analysis and dynamic code analysis on the current version code to obtain an initial dependency relationship graph and a runtime dependency relationship, and obtaining a target dependency relationship graph based on the initial dependency relationship graph, the runtime dependency relationship and the current change details; traversing each dependency relationship in the target dependency relationship graph by using a graph traversal algorithm to obtain a propagation path influenced by the current code change data, and determining a regression test range of the current version code by using the target pre-training model and the propagation path; and continuously integrating and deploying the assembly line to execute the test case set generated based on the regression test range, and completing the regression test to obtain a current regression test result. And the regression test efficiency and timeliness rate are improved.

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.

A system and method are provided for continuous integration, continuous deployment of a network component, such as a software-defined wide area network, a firewall, a router, or a load balancer. The network component is tested before deployment by acquiring data flows from a production environment and obtaining an acquired flow table that includes respective entries corresponding to types of data flows that are defined by header information (e.g., 5-tuples or pairs of source and destination addresses, depending at which layer in the OSI model the network component operates). First and second flow tables are generated for the first and second versions of the network component by applying simulated traffic (e.g., derived from the acquired data flows) to the respective versions of the network component. A comparison between the first and second flow tables is evaluated to determine whether second version of the network component can be deployed.

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.

Some examples of the present disclosure relate to risk analysis of test failures that occurred during a testing phase of a continuous integration pipeline. In one particular example, a system can detect a failure of a test during a testing phase of a continuous integration pipeline. The testing phase can involve executing a group of tests in a plurality of configurations prior to merging an update to source code into a code base of a software application. Based on detecting the failure of the test during the testing phase, the system can access historical data associated with prior executions of the test, determine a risk score associated with the failure of the test based on the historical data, and perform an action associated with the source code based on the risk score.

A system and method are provided for continuous integration, continuous deployment of a network component, such as a software-defined wide area network, a firewall, a router, or a load balancer. The software development lifecycle is achieved without interrupting the data flow of the network by using a multi-dataplane architecture, including a primary dataplane and a shadow dataplane. A packet dispatcher relays ingress data packets to the primary dataplane executing a current version of the network component and the shadow dataplane executing an upgrade to the network component. A control plane agent analyzes / compares the performances of the respective dataplanes for verification testing, and the control plane agent upgrades the network component to the new version upon passing the verification testing. The upgrades is achieved without interruption to the data flow of the network component by gradually transitioning to outputting egress data packets generated using the upgraded version.

A method by one or more electronic devices to provision an infrastructure for a machine learning application in a multi-tenant on-demand serving infrastructure. The method includes storing a plurality of templates, wherein each of the plurality of templates indicates a scoring interface, a web server, a definition of a continuous integration pipeline, and a definition of a continuous deployment pipeline, receiving a request to provision the infrastructure for the machine learning application using a specified template from the plurality of templates, and provisioning the infrastructure for the machine learning application using the specified template to create a version control system repository, a continuous integration pipeline, and a continuous deployment pipeline.

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.