85 results about "Pipeline (software)" patented technology

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 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.

Techniques are provided for failure mitigation in software deployment pipelines using generative artificial intelligence (AI). One method comprises obtaining a request to merge code changes associated with a first branch of software code with a second branch of the software code; in response to the request: obtaining information characterizing the software deployment pipeline; applying at least a portion of the information to a classification model to obtain a prediction that an implementation of the request will result in a failure; applying, in response to the prediction that the implementation of the request will result in the failure, at least a portion of the information to a generative AI model, with failure information characterizing reasons for the failure, to obtain mitigation actions to mitigate the failure; and automatically initiating processing steps associated with at least one of the mitigation actions to mitigate the failure.

A method for processing packets, the method comprising receiving a packet by a data processing unit (DPU), making a first determination that there is no exact match flow entry in a hardware switch pipeline in the DPU for the packet, in response to the first determination, classifying, in the DPU, the packet as a data packet, in response to the classifying of the packet: identifying, using a software data plane in the DPU, a forwarding information base (FIB) entry to be used to transmit the packet towards its intended destination, identifying, using the software data plane, a flow associated with the packet, initiating, using the software data plane, programming of an exact match flow entry in the hardware switch pipeline, and initiating, using the software data plane, transmission of the packet towards its intended destination using the FIB entry and the hardware switch pipeline.

In some implementations, a fault diagnosis system may track information associated with a first execution of a code pipeline to deploy an updated version of software associated with the code pipeline. The fault diagnosis system may receive an indication of a failure in a second execution of the code pipeline, wherein the second execution of the code pipeline is triggered by a commit of source code to a source code repository. The fault diagnosis system may determine whether the updated version of the software associated with the code pipeline or the source code committed to the source code repository is a cause of the failure in the second execution of the code pipeline. The fault diagnosis system may send a request to plan code development to resolve the cause of the failure in the second execution of the code pipeline.

The invention discloses an application software architecture for encapsulating a task management and synchronization mechanism of an operating system, and belongs to the technical field of industrial real-time control. According to the architecture, an operating system task management and synchronization mechanism is modularly packaged into three types of objects: an employee type for packaging specific business execution logic; the pipeline class is used for packaging inter-task communication and synchronization functions; and the behavior class is used for packaging an operating system task entity and is responsible for scheduling employee objects contained in the behavior class to be executed orderly. The three types of objects are created, assembled and started through a factory mode according to configuration information to form a complete real-time task system. According to the method, through object-oriented packaging and configuration assembly, the problems of tight task coupling and difficult code maintenance in traditional process-oriented programming are solved, loose coupling and high cohesion among task modules are realized, the configurability, maintainability and expandability of industrial real-time software are remarkably improved, and the method is suitable for various real-time operating system environments.

Annotating changes in software across computing environments, including: monitoring a software development and deployment pipeline; annotating a detected change in software with evidence providing information about one or more aspects of the detected change in software; and referring to the annotations for an unexpected change upon detecting the unexpected change in an environment that is outside of the software development and deployment pipeline.

The invention discloses a large language model softmax function hardware acceleration circuit and method, and belongs to the field of neural network hardware acceleration of super-large scale integrated circuits. An input sequence is divided into a plurality of data blocks which are processed in parallel, and three-stage pipeline division is adopted, so that average single calculation delay is shortened to G clock cycles, the calculation parallelism is improved, the calculation speed of a softmax function is improved, and the reasoning delay is reduced; and a sparse mask strategy of sparse threshold comparison is introduced, the sparsity of data is fully utilized, the problems of high calculation complexity and high calculation delay of the softmax function and the data access bottleneck of the softmax function are solved, the calculation cost is remarkably reduced, and the calculation efficiency is improved. In addition, a softmax function hardware circuit adaptive to software optimization is constructed, calculation delay and memory access pressure are reduced through the hardware circuit, and data processing efficiency is improved.

Described herein are systems utilizing a DevSecOps model comprising a CI / CD template, framework interfacing with a CI / CD pipeline that can integrate the assemblage of software components that collectively automate the building, testing, and delivery of applications within various hosting environments. Also described herein are methods utilizing the aforementioned systems.

The invention belongs to the field of software development, and provides a pipeline instance triggering method and device, a storage medium and electronic device.The method comprises the steps that a target pipeline is generated based on code change information and multiple mapping relations through a branch strategy adaptation component; and through an assembly line instantiation synchronization component, based on the called cloud effect assembly line instantiation service, sequentially executing assembly line synchronization operation, construction synchronization operation, cloud configuration binding operation and assembly line instance operation operation so as to trigger, start and operate a target assembly line instance corresponding to the target assembly line. Through the triggering method, the target assembly line can be automatically generated based on the code change information, and the target assembly line instance can be automatically triggered, started and operated.

The application discloses a software project dependency analysis method, which is applied to the technical field of software development. The method provided by the application comprises the following steps: adding a pre-developed scanning plug-in into a pre-deployed pipeline; searching for network addresses of at least one target software project to be subjected to dependency analysis in the pipeline; creating a corresponding thread for each target software project, and distributing an analysis task of the target software project to the corresponding thread; starting the thread, using the scanning plug-in to scan files of the target software project according to the network addresses, obtaining a target format file used for representing a type of the target software project, and obtaining the type of the target software project; and using the scanning plug-in to analyze the target format file according to the type of the target software project through the thread, and obtaining dependency information of the target software project.

A system for registration of a software application on a digital platform, having one or more processors, a memory storing program instructions which, when executed by the one or more processors, causes the processor to create a pipeline between the software application and the digital platform, wherein the pipeline provides an interface between the software application and the digital platform, define a template to capture one of more parameters of the software application, wherein the template is based on the pipeline created between the software application and the digital platform, capture the one or more parameters of the software application, wherein the one or more parameters are relating to one or more of indexing, packaging, versioning of the software application, define a resources requirement of the software application, wherein the resources are classified as initialization resources and deployment resources and the initialization resources are only created once at the registration of a software application, update the resources requirement by defining an order of creation of the resources, wherein the initialization resources are created prior to the creation of deployment resources and determine if the one or more parameters and the resources requirement of the software application are compatible with the digital platform and based on the determination, transmit the one or more parameters and the resources requirement of the software application to the digital platform, via the pipeline.

A hardware accelerator (10) is disclosed that can flexibly be configured to support differing data types and differing operation flows. The hardware accelerator includes a plurality of fixed tensor operation logic units (16), tensor operation pipeline logic (18) configured to receive from the processor a pipeline command (24) including a software-defined tensor operation pipeline definition (26) defining a plurality of tensor operation stages (30) in a tensor operation pipeline (32) and associated predetermined tensor operations to be performed at each of the defined tensor operation stages. The hardware accelerator is further configured to receive tensor data (28) to be computed by the tensor operation pipeline, and implement the tensor operation pipeline to perform the tensor operations in each of the tensor operation stages on the tensor data, to thereby produce a tensor operation pipeline result (34) for the tensor data, and output the tensor operation pipeline result to the processor.

The invention discloses a data processing method and device, electronic equipment and a computer program product. The method comprises the steps that a target server in a pipeline system receives target description information sent by a software development kit in a trusted area; when the target server receives the target event information, analyzing the target description information to obtain data processing demand information; the target server sends the target description information to a first node in the trusted area based on the data processing demand information, and the first node processes the to-be-processed data according to the target description information to obtain a processing result. According to the data processing method and device, the technical problem that in the related technology, when a user uses an assembly line service provided by a third party to carry out data processing, the third party needs to pull various data information of the user, and consequently the data security is poor is solved.

The invention provides an upper computer intelligent control system and method for a pipeline processing flexible production line, and the system and method achieve the full-flow intelligent control of the pipeline processing flexible production line from MES work order information to production instructions through the three-stage architecture design of a user interaction layer, a data processing layer and a hardware driving layer. According to the system, modular design is adopted, collaborative operation of station hardware equipment such as a feeding vertical warehouse, rust removal, cutting, carrying and welding is integrated, the operation state of each station and the posture of a welding robot are monitored in real time through a simulation interface, and the production safety and the process controllability are remarkably improved. According to the invention, cooperative control of each module of the production line and among the modules is realized, the production efficiency is improved, the time and labor cost is reduced, the problems of fine processing flow and difficult connection are solved, functional redundancy is reduced through customized design, the compatibility of software and hardware is improved, one-key operation is simplified, the learning cost is reduced, all readable interfaces are reserved, and the system is suitable for popularization and application. New function modules are convenient to add, and new programs do not need to be redesigned and developed.

The invention discloses an efficient and parallel autoregressive diffusion model hardware acceleration system based on an FPGA (Field Programmable Gate Array), and relates to the technical field of FPGA and machine learning. The system comprises a host end and an FPGA hardware end, the host end is responsible for preprocessing and post-processing of data, and the FPGA hardware end comprises a system control scheduling layer, a three-level storage structure layer and an autoregressive diffusion parallel architecture. According to the method, a collaborative pipeline design is adopted, deep parallelism of an autoregression Token generation process and a diffusion picture generation process is realized through a Token grouping adding mechanism, and a diffusion denoising process can be started without waiting for the completion of complete sequence generation. Besides, a universal attention processing hardware module is designed in the system, QKV projection and matrix multiplication are realized by using a systolic array, and calculation resources are optimized in cooperation with a pipelined Softmax calculation and delay normalization strategy. Through collaborative design of software and hardware, the problems of high reasoning delay and large resource overhead of the autoregression diffusion model in an edge computing scene are effectively solved, and the throughput and the energy efficiency ratio are remarkably improved while the generation quality is ensured.

Aspects of the subject disclosure may include, for example, obtaining a model file and a context file associated with an environment-as-code (EaC) service, committing the model file and the context file utilizing a source control software service, and in response to the committing, building a set of instructions as part of a pipeline associated with the EaC service for utilizing a continuous integration and continuous deployment (CI / CD) software application. Further embodiments may include assembling infrastructure-as-code (IaC) software from a curated library of software patterns based on the model file and the EaC service, configuring a group of workspaces for a IaC provider software application based on the set of instructions as part of the pipeline, and providing provisioning instructions to a cloud provider server to provision computing resources based on the group of workspaces. Other embodiments are disclosed.

Techniques are provided for software deployment pipeline evaluation using a presence of designated functions. One method comprises obtaining information characterizing a set of designated functions; determining one or more of: (i) whether at least one stage of a given software deployment pipeline comprises one or more of the designated functions in the set and (ii) whether an execution of one or more of the designated functions in the at least one stage of the given software deployment pipeline succeeded; and initiating one or more automated actions based at least in part on at least one result of the determining. The designated functions may be specified for different technology types. Job logs associated with the at least one stage of the given software deployment pipeline may be searched for commands that execute one or more of the designated functions.

A system for providing an API-driven continuous test platform is disclosed. The system may include one or more processors, a test engine, one or more test agents, and a database. The system may prepare (according to a configuration file) a first test configuration comprising a first selection of the one or more test agents, execute (using the test engine) the first test configuration to produce one or more first test results, and store (using the database) the one or more first test results. Finally, the system may process (using a continuous integration and continuous delivery (CI / CD) pipeline) the first test results by performing at least one of the following CI / CD processes: updating a central code base of an enterprise production environment, rejecting at least one code snippet processed by the test engine during execution of the first test configuration, and marking the first test results as inconclusive.

The invention relates to a universal FPGA (Field Programmable Gate Array) implementation method and system for accelerating multivariable recursive function operation, which is characterized in that the recursive function operation is simplified by using the characteristics of FPGA parallel operation and pipeline calculation, so that the calculation speed is improved, the calculation speed is high, the resource consumption is low, the consumed resource is controllable, the algorithm is obviously superior to a C language decoding algorithm, an IP (Internet Protocol) core is not used, cross-platform transplantation is convenient, and the universality is high. The data length and the recursion frequency can be flexibly configured through macro definition, so that the method is suitable for various algorithm application occasions, high-performance multivariable recursion function operation acceleration is achieved, a proper chip and a proper clock crystal oscillator can be selected according to calculation results of formulas such as operation time and resource consumption, and an overall software architecture can be planned.

This invention discloses an automated project iteration system based on AI agents and the GitLab MCP protocol. It comprises six core modules and is deeply integrated with the GitLab platform via the GitLab MCP protocol. It can receive natural language requirements, automatically analyze project context, generate compliant functional code, and simultaneously and intelligently generate multi-level verification scenarios and test cases, including unit tests and integration tests. It automates operations such as code submission, CI / CD pipeline triggering and monitoring, and merge request management, forming an end-to-end automated closed loop from requirement to verification success. This invention solves the problems of missing verification scenarios, fragmented tools, and cumbersome operations in existing development, significantly improving the efficiency and quality assurance capabilities of software development iteration.

A method for implementing a low code no code continuous integration / continuous delivery (CI / CD) platform includes receiving, by the CI / CD platform, a request to build a CI / CD pipeline for developing a software application. The method includes building, by the CI / CD platform, the CI / CD pipeline by at least: receiving a selection of one or more pre-built stages from a plurality of pre-built stages of the CI / CD pipeline and arranging, via the user interface, the selected one or more pre-built stages. The method also includes executing, by the CI / CD platform, the built CI / CD pipeline to develop the software application by at least: retrieving, from a database in communication with the CI / CD platform, code for each of the arranged one or more pre-built stages. Related systems and articles of manufacture are provided.

The present disclosure provides a method (300) and a system (130) for error detection and correction in continuous integration / continuous delivery (CI / CD) pipelines. The system (130) executes a CI / CD pipeline based on updates to a software received from one or more development devices (110) and detects errors in the updates and / or in the code stored in a repository (220) by processing log data generated during the execution of the CI / CD pipeline using a local AI model (140).The system (130) then executes a first error correction script generated by the local AI model (140) to correct the detected errors, and iteratively executes a second error correction script generated by an external AI model (150) for errors that persist after the implementation of the first error correction script, until a stop condition is met.

Apparatus for a dynamic development operations (“DevOps”) pipeline generation may include the current build and release of the source code files and related changed units within a data repository. The data repository may include a plurality of data associated with the current build and release of the source code file. A software system may collect the data from the repository. The collected data may be input into an artificial intelligence or machine learning (“AI / ML”) module. The AI / ML module may use a language learning module (“LLM”) to create a plurality of nodes from the data. The LLM may create a knowledge graph from the nodes. The knowledge graph may be input into a quantum computing system to create attention matrices. The attention matrices may be input into a quantum annealing system to determine the DevOps plan. A transformer neural network (“TNN”) may output and execute the DevOps plan.

The invention relates to the technical field of electronic information and software engineering, in particular to a double-pipeline cooperation and backpressure control method for a multi-card PCIe acquisition system. The method comprises a data acquisition pipeline and a control / monitoring pipeline, the data acquisition pipeline executes sampling, DMA carrying and frequency spectrum / time domain updating, and the control / monitoring pipeline executes state sampling and prompt event generation based on a backpressure index and drives a front-end interface to perform linkage display through an asynchronous signal. According to the method, the system data loss rate and the interface refreshing delay are remarkably reduced, and the operation response speed and the system operation and maintenance performance are improved.

The application discloses a method for preventing a return-oriented programming (ROP) attack, and the method comprises the following steps: when processing instructions in a pipeline stage mode, if a first target instruction in a function call and a target pipeline stage are identified, the target pipeline stage is paused; the first target instruction is a first set instruction for indicating that a stack pointer points to a caller stack frame before and after execution, or a second set instruction for indicating that a stack pointer points to a callee stack frame before and after execution; a return address, a stack pointer and a key of the function call are acquired by an instruction bypass unit, and a PAC is generated; the PAC is added to reserved bits of the return address, so that a return address carrying the PAC is obtained; and after the instruction bypass unit is executed, the first target instruction is processed by the target pipeline stage. By using the method, the PAC can be generated by using the return address, the stack pointer and the key, and the PAC is added to the return address, so that the ROP attack is prevented, and the security of software is improved.

A system and method are provided for automating software development documentation using multi-agent artificial intelligence. The disclosed systems and methods may include cloud-based infrastructure, an AI processing pipeline, and an integration framework. The system and method are automated to analyze code changes in real-time, generate appropriate documentation, and ensures accuracy through a novel verification process, resulting in significant time savings and improved consistency and fidelity in software development workflows.

The invention discloses a BIM-based electromechanical installation pipeline three-step deepened design method, and particularly relates to the technical field of pipeline design, and the method comprises the steps: building a main pipeline model of an electromechanical system through BIM software, building a basic pipeline frame based on data input and three-dimensional modeling, and carrying out preliminary adjustment to ensure that the actual requirements of a project are met; on the basis of a main pipeline model, detailed arrangement scheme design of pipelines is performed, reasonable layout of an electromechanical system is ensured by comprehensively considering functionality and safety of the pipelines and coordination of the pipelines and building space, secondary adjustment is performed on the model, conflict points in the model are identified and processed by using a BIM analysis tool and an algorithm, and the optimal layout of the electromechanical system is realized. According to the method, a deepened model is established, a high-precision construction drawing is automatically generated according to the deepened model, the designed BIM pipeline model is simulated in a digital twinning environment, the performance of the BIM pipeline model is quantified, and fine design and accurate evaluation of the electromechanical installation pipeline are facilitated.

Systems and methods for fuzzy regression testing of a machine learning pipeline. The pipeline includes one or more supporting software packages and executes a machine learning model. Reference artifacts associated with the pipeline are obtained. Subsequently, one or more test script is executed to compare test artifacts generated during execution to the reference artifacts.

A machine learning model development and deployment method and apparatus based on an MLOps platform, which method and apparatus relate to the technical field of computer software. The method comprises: creating, in a user interface or an API of an MLOps platform, a target experimental task for iteratively training a machine learning model, and performing target parameter configuration on the target experimental task, so as to obtain a configured target experimental task; executing the configured target experimental task in a Kubernetes cluster on the basis of training sample data, so as to obtain a machine learning model having optimal performance, and recording model parameters generated by the machine learning model during the execution of the configured target experimental task; deploying, on the basis of the deployment and management capabilities of Kubernetes, the machine learning model having optimal performance to a production environment, and by means of the API, enabling the machine learning model having optimal performance to provide external services; and on the basis of a continuous integration / continuous deployment pipeline, automatically deploying the latest trained machine learning model in the Kubernetes cluster to the production environment, so as to replace the machine learning model having optimal performance. The development and deployment efficiency is improved.