156 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 artificial intelligence crossing, in particular to an API test case rapid generation method and system based on an AI large model.The method comprises the following steps that joint analysis and semantic fusion are conducted on an API document and a demand document through a multi-modal data analysis layer, and a complete test context is constructed; generating a test case based on the pre-trained large model by using a hybrid inference engine; the method has the beneficial effects that the association constraint of the API document and the demand document is automatically extracted through semantic understanding; a parameter combination strategy is optimized through reinforcement learning, and the problem of combination explosion is solved; constructing a self-adaptive test case maintenance mechanism, and responding to API version iteration; an executable test code that can be directly integrated into the CI / CD pipeline is generated.

In some implementations, a code testing device may receive, from a first device associated with a developer, a first instruction to merge a first code branch. The code testing device may perform a first set of tests, associated with a first environment, in response to the first instruction. The code testing device may merge the first code branch based on the first set of tests resulting in a passing outcome for the first code branch. The code testing device may receive, from a second device associated with a reviewer, a second instruction to deploy the merged first code branch. The code testing device may perform a second set of tests, associated with a second environment, in response to the second instruction. The code testing device may deploy the merged first code branch based on the second set of tests resulting in a passing outcome for the merged first code branch.

Systems and methods for error mitigation in a software pipeline. In some aspects, the system receives a request for evaluating a pipeline at a recurrent interval of time. The request comprises a first version of software, a second version of software, evaluation tests to execute on the second version of the software at intermittent intervals, and the recurrent interval of time. The system, in response to determining the recurrent interval of time elapsed, executes the evaluation tests on the second version of the software. The system determines that the output of the evaluation tests does not match one or more target values. The system terminates deployment of the second version of the software. The system generates instructions to replace the second version of the software with the first version of the software and transmits a notification to a remote device.

In an implementation of a computer-implemented method: to create extracted data records, an extract filter is instructed to extract relevant data records from log messages of two runs of a software pipeline. To create diff records using the extracted data records, a diff filter is instructed to compare and identify differences in messages between the two runs, where the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from. A recommendation engine is instructed to execute a machine-learning model training with the diff records. The recommendation engine is called to analyze the diff records for a failure-indicator. A determination is made that a failure causing the failure-indicator has been corrected in a later run of the software pipeline. A change is identified in a configuration or version of a software application associated with a correction. A failure-indicator-solution combination is generated.

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.

Techniques are provided for visual generation of software deployment pipelines using recommendations of development and operations (DevOps) blueprints. One method comprises obtaining information characterizing a technology type and a programming language for a selected software development project; recommending a DevOps blueprint for the selected software development project based on a technology type and a programming language for the selected software development project, wherein the DevOps blueprint comprises reusable software development resources for the technology type and the programming language; providing a graphical interface to present a visual representation of the reusable software development resources; obtaining information characterizing interactions of a user with the graphical user interface to select the reusable software development resources for a pipeline associated with the selected software development project; and automatically generating software code in a target programming language of the DevOps collaboration tool based on the reusable software development resources in the pipeline.

The invention relates to a simulation calculation method, device, equipment and medium for an anti-impact system with a pipeline, and belongs to the technical field of simulation computation.The simulation calculation method for the anti-impact system with the pipeline comprises the steps that geometric models of all parts of the anti-impact system with the pipeline are established, and the geometric models are imported into preset simulation software for preprocessing operation; modal analysis is carried out on the geometric model after preprocessing operation, and modal parameters of the anti-impact system with the pipeline are obtained; performing spectral analysis on the geometric model after the preprocessing operation based on the modal parameters to obtain an impact response of the anti-impact system with the pipeline; and according to the impact response, adjusting component parameters of the anti-impact system with the pipeline. According to the method, accurate simulation and calculation are carried out on the impact-resistant system with the pipeline, the component parameters of the impact-resistant system with the pipeline are adjusted, and the impact resistance of the impact-resistant system with the pipeline is improved.

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 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.

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 systems and methods disclosed herein relate to a model validation platform that enables dynamic validation of a user's prompt for a large language model (LLM) in order to evaluate the validity of the prompt and the suitability of a large language model for processing the prompt. For example, the platform enables an estimation of the resource allocation associated with processing the prompt with a given LLM, as well as a modification of the prompt, prior to the processing the prompt with the selected LLM. The platform can further validate the output prior to transmitting the output to a server system for display to the user. By doing so, the platform enables dynamic evaluation of a request to execute an LLM, as well as evaluation of resulting outputs, for accuracy and efficiency improvements in data processing or software development pipelines.

In one example, a computer system can generate a graphical user interface (GUI) for forecasting software including a drag-and-drop canvas with a set of rearrangeable nodes defining a forecasting pipeline. The computer system can detect a user interaction for attaching an external-language execution node to the pipeline, which can be used to insert custom code defined using an external programming language. The computer system can receive the custom code. The computer system can receive a user input to initiate execution of the pipeline. The computer system can generate wrapped custom code by augmenting the custom code with additional program code including shared variables. The computer system can provide the wrapped custom code to a set of execution threads configured to execute the wrapped custom code as part of the pipeline to generate one or more forecasts. The computer system can output the forecasts in the GUI.

A generative artificial intelligence (AI) pipeline has been created that employs aspects of natural language processing (NLP) to detect intents of web API calls and then summarizes the behavior expressed by the collective of intents. The pipeline uses a lightweight language model for intent classification of URLs corresponding to API calls in a time interval. The pipeline associates the intent classifications with metadata corresponding to the URLs and feeds this into another lightweight language model that summarizes the intent classifications and metadata. The natural language summarization describes exhibited behavior that can be understood by a wider audience than security experts. The capability to detect intents of API calls occurring in network traffic increases visibility and control of user behavior, particularly in Software-as-a-Service (Saas) environments. Furthermore, the enhanced visibility of user behavior with the created pipeline recognizes new and previously unseen API calls from live network traffic at enterprise scale.

A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is represented in a data flow graph (DFG). The represented pipeline is subject to annotations, with the annotations including pipeline nodes and object references. The pre-processed pipeline is subject to execution or processing with the annotated object references capturing object lineage. Output from the executed pipeline is constructed and a corresponding control signal is formatted to dynamically and selectively control an operatively coupled physical hardware device or software.

Data processing tools often use pipeline-based workflows, which consist of a sequence of operations. Each operation is configured according to configuration settings provided by a pipeline developer. The operations may use other software components such as frameworks that may also be configured. An application developer that defines a series of operations to be performed to achieve a desired result is able to provide the configuration settings for the operations. However, the application developer may not have the expertise to efficiently define configuration settings for the underlying frameworks. As discussed herein, a pipeline configuration system is used to generate configuration settings for frameworks used by a pipeline based on configuration settings for the operations of the pipeline. The operation configuration may include non-transformable properties, transformable properties, and internal properties. The pipeline configuration system may primarily modify the transformable properties.

With invocations of a software development pipeline, organization specific remediations / fixes for a software project can be learned from scanning results of code submissions (e.g., commits or merges) across an organization for a software project(s). Fixes of detected program code flaws can be detected and / or specified across scans and associated with flaw identifiers and used for training machine learning models to identify candidate fixes for detected flaws. This ongoing learning during development propagates fixes created or chosen by experts (e.g., software engineers working on the software project) relevant to the software project. The experts can choose from suggestions mined from the learned fixes of the organization and suggestions generated from a pipeline created with the trained machine learning models. The selections are then used for further training of the machine learning models that form the pipeline.

The invention provides a heterogeneous acceleration method and system based on a parallel gene sequence alignment algorithm, and the method comprises the steps: S1, dividing DNA into n fragments, and enabling a CPU end to read the fragment data of the DNA; s2, performing parallel multi-thread processing on the fragment data at a CPU (Central Processing Unit) end to generate input data; s3, predicting the execution time of the corresponding thread at the software CPU end and the execution time of the corresponding thread at the hardware FPGA end; s4, according to the predicted execution time, selecting a position where the input data continues to be executed; s5, if acceleration processing is carried out at the FPGA end, the processed data are stored in a global memory; and S6, enabling the CPU end to perform parallel sequence alignment on the data in the global memory to complete sequence alignment. According to the invention, a strategy for realizing parallel processing of a plurality of to-be-aligned sequences based on software multithreading is realized, and the FPGA equipment end realizes parallel and streamlined processing of a plurality of sequences based on multiple cores.

The invention provides a compiling method, a compiling cache configuration method and device, electronic equipment and a storage medium, and the compiling method comprises the steps that based on a received compiling request, a compiler cache tool corresponding to an integrated tool assembly line is operated, and the integrated tool assembly line is used for software development and has a compiling cache function; whether compiling cache data matched with the compiling request are cached in a shared directory of the server or not is checked through the compiler cache tool, if yes, the compiling cache data are backed up and cached into the integration tool assembly line, and the compiling cache data comprise a first intermediate file generated by the last compiling operation; and executing a compiling operation on the compiling cache data and the data corresponding to the compiling request through the integration tool assembly line to obtain a compiling result. The compiling efficiency can be improved.

The invention discloses a BIM (Building Information Modeling) technology-based pipeline hole reserving design system and method. The method comprises the following steps of defining hole occupation data for storing hole parameters; according to each hole occupation data read from the storage of the project file, creating a corresponding hole volume in the BIM software as a BIM model component; calculating the geometric dimension of the volume of each hole; combining the masses of the adjacent holes; and updating corresponding hole occupation data according to the combined hole volume, and generating a hole instance of the multi-view BIM hole according to the updated hole occupation data, thereby realizing synchronization of the BIM hole and the two-dimensional drawing. According to the method, automatic circulation of pipeline hole reserving from data to models and drawings is realized; the problems that omissions are easy to occur in manual marking and no professional discrimination is available for plug-in hole opening are solved, the design quality is guaranteed, the design period is shortened, and information mistakes and omissions are reduced; and multiple views are synchronously generated, so that inconsistency of manual labeling is avoided, and construction application is directly supported.

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.

A system and method for securing an enterprise's software supply chain includes an enterprise, a build artifact, and a metadata extractor. The metadata extractor is configured to extract metadata from the build artifact and send the metadata to an enhanced software bill of materials generator. The enhanced software bill of materials generator may generate a software bill of materials based on the metadata. A know your system large language model (“KYS LLM”) is also provided. The KYS LLM is configured to be trained by the metadata and to receive a query from an actor. An enterprise blockchain is provided and may receive the metadata and a query. The enterprise blockchain may include an application programming interface configured to initiate a download of dependencies from the enterprise blockchain. A pipeline may be provided to receive the download of the dependencies from enterprise blockchain and deploy information from the download.

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.

In an example, an apparatus may operate as a continuous delivery (CD) pipeline for application artifacts running on a target software platform. The apparatus may include a processor to operate an application upgrader for the target software platform, the application upgrader to: consume application artifacts from a continuous integration (CI) system, wherein the target software platform runs a version of the application artifacts; obtain a signed manifest output from the CI system, the signed manifest identifying the consumed application artifacts; and compare a signature of the application artifacts from the CI system to a signature of the version running on the target software platform to determine if an update of the version running on the target software platform is required. Other embodiments may be disclosed and / or claimed.

Systems and methods for alert fixing. A method includes creating an entity graph based on correlations among software components of a software development infrastructure. The entity graph includes representing the software components. A software development pipeline is mapped in the entity graph by enumerating pipeline execution steps with respect to the software components of the software development infrastructure. Correlations between software components indicated in alerts are determined based on the entity graph. The alerts are deduplicated by matching between alerts between the alerts based on the correlations. One or more fix actions are generated based on the deduplicated alerts. The software development infrastructure is secured by causing implementation of the fix actions.

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.