257 results about "Software deployment" patented technology

A vehicle software deployment management system generates a modifiable deployment plan and one or more associated vehicle software modules that is sent to an edge device at a vehicle activity site. The edge device stores the modifiable deployment plan and the one or more vehicle software modules for updating a given vehicle at a future time. In some embodiments, the vehicle may have insufficient network connectivity to perform the update remotely from the vehicle activity site. In some embodiments, the modifiable deployment plan may be modified by the edge device at the future time when the vehicle is present at the vehicle activity site, based on vehicle information obtained by the edge device and based on vehicle user / technician input obtained by the edge device.

Using large language models (‘LLMs’) for code hardening in a storage system, including: creating, in a storage system, a cloned software deployment based on a software deployment executed in the storage system, wherein the cloned software deployment is associated with a different tenant of the storage system than the software deployment; generating, using a large language model (LLM) and based on input data comprising data describing an error in the software deployment, a code update to fix the error; verifying the code update, including updating the cloned software deployment based on the code update; and updating the software deployment based on the verified code update.

The present disclosure pertains to systems, computer program products, and methods for an automated mesh service-based deployment intelligence system. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

Systems and methods in accordance with embodiments of the invention can analyze a variety of software applications, modify the software applications, and / or automatically deploy the software applications to a distributed computing system. Distributed computing systems can provide software applications in a scalable, low cost manner that can be dynamically scaled to demand. Software deployment systems in accordance with embodiments of the invention can automatically process software applications to determine the suitability of the software application to be deployed to a distributed computing system and / or modify the software application for deployment. A variety of machine classifiers can be used to score various aspects of a software application to identify portions of the application for modification and / or suitability for deployment.

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.

Techniques are provided for evaluation of software deployment pipeline stages by applying software testing checkers. One method comprises obtaining software testing checkers for evaluating software items in a software deployment pipeline; in response to a request to transition the software deployment pipeline from a first stage to a second stage, automatically applying at least some of the software testing checkers to the first stage of the software deployment pipeline to obtain respective software testing checker results; and initiating an automated action based at least in part on the software testing checker results. A transition to a next stage of the software deployment pipeline may be permitted (or denied) based on the software testing checker results associated with a prior stage of the software deployment pipeline. A given software testing checker result may be processed using one or more specified rules.

Techniques are provided for automated evaluation of software items of a software deployment pipeline. One method comprises, in response to a request to add a software item to a software deployment pipeline: determining an approval status of the software item for inclusion in one or more stages of the software deployment pipeline based on an evaluation of designated properties of the software item relative to designated criteria; and initiating an automated action based on at least one result of the determining. The designated properties (such as a version and / or an approval status) of the software item are specified for a particular product. Real-time changes may be applied to the designated properties of the software item. The software item may be classified into a designated license category having associated license guidelines. Metadata for the software item may be based on the designated license category.

Techniques are provided for generation of a software deployment pipeline manifest using software item metadata. One method comprises obtaining software items, requested for inclusion in a software deployment pipeline, and corresponding metadata for at least one of the requested software items, from a software repository; in response to a request to transform a given stage of the software deployment pipeline, generating a manifest for the given stage, wherein the manifest identifies the software items in the given stage and comprises the corresponding metadata for the software items in the given stage; and storing the manifest for the given stage in the software repository. The storage of the software items in the software repository may comprise evaluating, prior to the storage: (i) a presence of vulnerabilities in the software items and (ii) a license review status of the software items.

The invention discloses an open source component blood relationship analysis method and system, and relates to the technical field of software supply chain security, and the method comprises the following steps: S1, automatically discovering an open source component; s2, analyzing software components; s3, open source component fingerprint analysis; s4, an SBOM material list is generated; and S5, open source component blood relationship analysis. According to the method, comprehensive tracking of a source code data processing process is realized through a password hash function and a cosine similarity algorithm, through a similarity analysis technology, when a component is modified, the component can be quickly identified, a platform can regularly collect file fingerprint features of a common open source component, and through combination with an extracted open source software dependency package, the component can be quickly identified. After feature values are extracted, stored and compared, the code detail tampering behavior existing in the jar package is recognized, the open source component risk is positioned, and the controllability and safety management of the software deployment package are improved.

The invention discloses an agent-free honeypot mapping method and device, and relates to the technical field of network security. The method comprises the following steps: analyzing metadata of core switching equipment, and establishing a domain-address mapping matrix; periodically detecting an address space, constructing an IP asset dynamic portrait and generating an asset state map; deploying a cross-domain traffic forwarding protocol stack and a virtual domain identifier mapping rule; deploying a software-defined virtual switching unit and accessing the virtual honeypot probe cluster; allocating idle addresses for the probes and establishing a binding relationship; and periodically detecting and triggering a self-healing process when an address conflict occurs. The device comprises a core exchange and domain management unit, an IP asset detection unit, a network protocol configuration unit, a probe virtualization management unit, an address resource scheduling unit and a system cooperative control unit. According to the method, agency-free and automatic deployment of the honeypot is realized, and the problems that a traditional scheme depends on agency software, deployment is complex and IP conflict processing lags are solved.

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 method for automated deployment of application servers in a contact center system according to one embodiment includes generating a deployment package for the application servers in the contact center system based on deployment sizing information and environment information associated with the contact center system preparing a temporary deployment server of the contact center system for deployment of software to the application servers, and installing the software to the application servers using the temporary deployment server.

The invention discloses a demand development method, device and equipment. The method comprises the following steps: calling a demand analysis agent to analyze a natural language demand into a structured function demand unit comprising an input specification, an output specification and an execution constraint; the calling code generation agent generates source codes corresponding to the development requirements according to data type definitions and function calling specifications in the structured function requirement unit and the local project code library; calling a code test agent to generate and execute a test case based on the source code and the structured function demand unit; and after determining that a software deployment condition is met based on a test result, calling a code deployment agent to generate a deployment script and configuration information, and submitting the deployment script and the configuration information to a deployment execution engine to complete automatic deployment. According to the application, end-to-end automatic development from natural language requirements to application deployment can be realized.

The invention discloses a software deployment method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring basic information of to-be-deployed software; according to the basic information, generating a code library interaction instruction, so that the software deployment system uses the code library interaction instruction to obtain the to-be-deployed software, and the software deployment system compiles the to-be-deployed software to obtain a corresponding mirror image file; and obtaining resource configuration information of the to-be-deployed software, and determining a deployed target container according to the resource configuration information, so that the software deployment system generates a deployment arrangement process for deploying the to-be-deployed software in the target container and executes the deployment arrangement process based on the mirror image file. According to the implementation mode, full-automatic software deployment is realized, the problem that manual intervention is needed in existing software deployment is solved based on the capability of generating the code library interaction instruction and automatically determining the target container, the deployment efficiency is improved, the deployment process is simplified, and labor force is liberated.

The invention belongs to the technical field of software deployment, and particularly discloses a Linux system equipment software remote deployment method and system and a program product, a software remote deployment mode is adopted, configuration files and operation commands compiled by a user are analyzed through a client side, all deployment operation commands are responded in sequence, and a Linux system host is remotely connected in a butt joint mode. Corresponding deployment process instructions are compiled and executed, so that deployment operations such as receiving verification, software version replacement and program restarting of software files are automatically completed. According to the method, human errors can be avoided, the software deployment operation process is simplified, time consumed by developers for updating software versions can be effectively saved, the software development efficiency is improved, meanwhile, instructions in the deployment process are transferred and compiled based on the compiling tool, and transmitted software files are verified, so that the software development efficiency is improved. And the security and reliability of the software deployment process can be effectively ensured.

The techniques described herein provide a risk assessment framework that enhances the functionality of software deployment systems in cloud-based platforms. Generally described, the present techniques evaluate and consolidate various risk scores to classify a given computing cluster within a software deployment strategy. In various examples, a deployment system collects node-level feature data from the computing cluster to generate a dataset to train a prediction model to calculate constituent risk scores. In another aspect, the deployment system aggregates constituent risk scores to determine an overall risk of software failure. Likewise, the deployment system considers diverse criteria such as virtual machine size and virtual machine density to determine an overall impact of software deployment failure. The deployment system then calculates a composite risk score for the computing cluster as a function of the risk of software deployment failure and the impact of software deployment failure.

The invention discloses a method for acquiring server error information by a BMC (baseboard management controller), and relates to the technical field of fault diagnosis, and the method comprises the following steps: mapping a host serial port to the BMC based on a CPLD (complex programmable logic device), assisting in judging a server stage and debugging the serial port through the CPLD, collecting starting data by the BMC in a power-on starting stage, and sending the starting data to the BMC; and data collection and error early warning are carried out according to serial port jack input or BMC in the operation stage. The technical problem that in the prior art, server error information collection has high requirements for hardware support and software deployment is solved, and the technical effect of simplifying the server error information collection process is achieved.

Systems, devices, methods, and computer readable media for determining potential issues with a software upgrade are disclosed. In one implementation, the disclosed system includes at least one processor and at least one non-transitory memory storing instructions to perform operations when executed by the at least one processor. The operations include training a machine learning model on historic trouble ticket data; receiving data relating to a planned software upgrade; providing the received data to the trained machine learning model, wherein the trained machine learning model generates a tag value; calculating a risk score based on the tag value; and determining a client-specific action to be performed relating to the planned software upgrade and based on the risk score.

A method includes identifying configuration information of a server being deployed, which includes a configuration and software requirements, and the server is managed by a management server through a management controller controlling a host hosting the server. The method includes retrieving a recommended setup of hardware and software for the server based on the configuration information and retrieving, from a deployment tracking module, a recommended deviation. The method includes creating a deployment file based on the recommended setup and deviation, and transmitting the deployment file to the management controller, which uses the deployment file to configure the server. The deployment tracking module monitors firmware, drivers and / or software deployments of servers for problems and software deployments, monitors websites / databases associated with firmware, drivers and / or software deployed on the servers for updates, analyzes information from monitoring the deployments and updates from the websites / databases, and manages recommended deviations from recommended setups.

The utility model relates to an AFE-based battery management system, which comprises a battery equalization driving circuit and an MCU unit, the battery equalization driving circuit comprises an AFE chip, a current balancing circuit, a charging control enabling circuit and a discharging control enabling circuit, the AFE chip is responsible for converting analog signals such as voltage, current and temperature of a battery into digital signals, and the MCU unit is responsible for outputting the digital signals. The MCU is connected with the AFE chip through an I2C bus, the MCU can conveniently read battery data collected by the AFE chip through the I2C bus and issue a control instruction according to needs, and meanwhile, due to the design, software deployment is also facilitated, and through coordinated control of the AFE chip and the current balancing circuit, the battery data can be further processed by the MCU, so that the service life of the battery can be prolonged, and the service life of the battery can be prolonged. And high-efficiency equalization of each single battery in the battery pack is realized through the internal current balancing circuit and the external current balancing circuit, so that each single battery can work in an optimal state, thereby improving the overall performance of the battery pack and prolonging the service life of the battery pack.

A greenhouse gas (GHG) emissions estimation system is designed to train and utilize an artificial intelligence (AI) model to estimate the GHG emissions associated with operating a software application that is deployed (or is intended to be deployed) within a virtualized environment. For example, a software developer may utilize the GHG emissions estimation system as part of a cost / benefit analysis of different software solutions at an early stage of application design and development. Since there may be a variety of possible infrastructural options for a given software application, the GHG emissions estimation system enables the developer to estimate the GHG emissions associated with each infrastructural option, which can be used when determining which solution should be developed and deployed.

The invention provides a system installation and deployment method and system based on engineering metadata, and relates to the technical field of software deployment, and the method comprises the steps: determining a software installation mode, and obtaining the engineering metadata; checking an installation environment based on preposition dependency information prompted by the engineering metadata; after the safety of the installation environment is ensured, performing signature verification and integrity verification on the software package based on the engineering metadata abstract and the signature information; after verification and verification are passed, the software package is installed on the basis of the component list of the engineering metadata, and the software package is configured on the basis of the information configuration list of the engineering metadata; and acquiring a security policy of the engineering metadata, activating a security protection function, generating installation engineering metadata after the security policy is completed, and evaluating the installation process based on the security engineering metadata.

The invention discloses an automatic deployment method, device and equipment for Linux environment software and a medium, and relates to the technical field of software deploy.The method comprises the steps that Linux environment information is collected on the basis of a programming language and in combination with a Linux system command, and the collected Linux environment information is stored as structured data; creating an improved model, and training by adopting pre-collected Linux environment software deployment case data, so that the trained improved model outputs a deployment strategy based on the Linux environment information; and according to the output deployment strategy, downloading an installation package of the software to be deployed and carrying out analysis processing on a software installation dependency relationship so as to realize automatic installation and deployment of the software based on scripts. According to the method, the software deployment in the Linux environment can be efficiently realized, the deployment error rate is reduced, and the adaptability is enhanced.

The invention relates to a software deployment technology, in particular to a remote deployment method for a software platform user interface, which comprises the following steps: constructing an independent service process at a server, and providing an HTTP (Hyper Text Transfer Protocol) service by the service process; the service process communicates with a software platform process in real time to obtain a configuration file of a software platform user interface and operation data of a software platform application program, and the software platform is a third-party software platform; analyzing the configuration file of the software platform user interface through the service process, and locally generating a webpage containing the corresponding user interface at the server according to the information of each user interface in the configuration file; when the webpage is opened in a local browser of the server side, the service process obtains an opening behavior so as to refresh the operation data of the application program of the software platform to the webpage in real time; and / or, the service process responds to the user interface operation on the webpage so as to transmit an operation instruction to the software platform in real time, and then corresponding operation is executed through the software platform.

The invention discloses an incremental configuration file synchronous release method and device, computer equipment and a storage medium, and the method comprises the steps: executing an environment configuration operation on a code warehouse to generate an initial configuration file; carrying out construction processing on the configuration file in a deployment platform to form a task file which can be used for automatic deployment and automatic release; writing a newly added configuration item into the configuration file to generate a target configuration file; and according to the task archive, executing a synchronous publishing operation on the target configuration file on a deployment platform. By implementing the method disclosed by the invention, a tester does not need to manually debug and deploy scripts and verify environmental parameters, so that invalid human input during multi-environment adaptation is avoided, the software deployment period is effectively compressed, the possibility of operation negligence is reduced, deployment failure caused by operation sequence deviation during manual deployment is avoided, and the deployment efficiency is improved. The technical scheme can be applied to the financial field.

The invention provides an application software remote deployment method and device of a railway signal general safety system, and relates to the technical field of software deploying.The method comprises the steps that firstly, network transmission protocols supported by all servers and all clients in the railway signal general safety system are determined; then deploying the network transmission protocol to all the servers and all the clients; and finally, remotely deploying the application software in all the servers by using any one of all the clients. According to the application software remote deployment method of the railway signal general safety system, storage hardware equipment is not needed, manual updating on each server is not needed, time and resources are saved, and convenience, safety and isolation of remote deployment of the application software of the railway signal general safety system are improved.

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.

The embodiment of the invention provides an updating instruction sending method and device, a storage medium and electronic equipment, and relates to the field of computers.The updating instruction sending method comprises the steps that server information of a target server is obtained through an information collection program, and the server information comprises software information in the target server; determining a corresponding software installation package set according to the server information; an updating instruction is sent to the target server, the updating instruction is used for indicating that the operating system of the target server is updated into the target operating system, and after the operating system of the target server is updated into the target operating system, corresponding software deployment operation is carried out on the target server according to the software installation package set. By adopting the technical scheme, the problem that the efficiency of migrating the operating system is relatively low is solved.

The invention provides a virtual machine index data monitoring method, device and equipment and a medium, and relates to the technical field of computers.The method comprises the steps that initial index data of a virtual machine is obtained from virtual data platforms for the virtual data platforms of multiple manufacturers; the virtual data platform is used for collecting initial index data of the virtual machine; performing unified processing on the initial index data corresponding to all the virtual data platforms to obtain monitoring index data corresponding to all the initial index data; and uploading the monitoring index data to a monitoring data service platform for monitoring. According to the technical scheme, the initial index data of the virtual machine is obtained from the virtual data platform of the manufacturer, software does not need to be arranged on the virtual machine, virtual machine resources are saved, monitoring flexibility is improved, and the situation that the index data cannot be collected due to software deployment failure is avoided.

A method performed by a system including a collector unit, an analyser unit, and a scheduler unit, comprises receiving a data comprising at least one of a hardware data, a traffic data and a service data related to one or more devices and one or more applications, feeding the data a trained model, determining, through the trained model, one or more patterns of the data, providing, an output from the trained model to the scheduler unit, performing, through the network, software deployment on the one or more devices based on the output.