公开(公告)号：IL297210B1

公开(公告)日：2025-03-01

申请号：IL29721022

申请日：2022-10-11

Applicant: IBM ,  JINHO HWANG ,  MUHAMMED FATIH BULUT ,  ALI KANSO ,  SHRIPAD NADGOWDA

Inventor： JINHO HWANG ,  MUHAMMED FATIH BULUT ,  ALI KANSO ,  SHRIPAD NADGOWDA

IPC: G06N20/00

Abstract: An artificial intelligence (AI) platform to support a continuous integration and deployment (CI/CD) pipeline for software development and operations (DevOps). One or more dependency graphs are generated based on application artifacts. A machine learning (ML) model is leveraged to capture a relationship between components in the dependency graph(s) and one or more pipeline artifacts. Responsive a change of an application artifact, the captured relationship is leveraged to identify an impact of the detected change on the pipeline artifact(s). The CI/CD pipeline is selectively optimized and executed based on the identified impact to improve the efficiency of the pipeline and the deployment time.

公开(公告)号：GB2615046B

公开(公告)日：2025-01-08

申请号：GB202306536

申请日：2021-08-31

Applicant: IBM

Inventor： MUHAMMED FATIH BULUT ,  MILTON H HERNANDEZ ,  ROBERT FILEPP ,  SAI ZENG ,  STEVEN OCEPEK ,  SRINIVAS BABU TUMMALAPENTA ,  DANIEL S RILEY

IPC: G06Q10/06 ,  G06F21/57

Abstract: Systems and techniques that facilitate automated health-check risk assessment of computing assets are provided that can generate a baseline health-check risk score that corresponds to non-compliance of a computing asset with a stipulated control, and can adjust the baseline health-check risk score based on a weakness factor of the stipulated control, an environmental factor of the computing asset, a criticality factor of the computing asset, and a maturity factor of the computing asset.

公开(公告)号：IL297210A

公开(公告)日：2022-12-01

申请号：IL29721022

申请日：2022-10-11

Applicant: IBM ,  JINHO HWANG ,  MUHAMMED FATIH BULUT ,  ALI KANSO ,  SHRIPAD NADGOWDA

Inventor： JINHO HWANG ,  MUHAMMED FATIH BULUT ,  ALI KANSO ,  SHRIPAD NADGOWDA

IPC: G06N20/00

Abstract: An artificial intelligence (AI) platform to support a continuous integration and deployment (CI/CD) pipeline for software development and operations (DevOps). One or more dependency graphs are generated based on application artifacts. A machine learning (ML) model is leveraged to capture a relationship between components in the dependency graph(s) and one or more pipeline artifacts. Responsive a change of an application artifact, the captured relationship is leveraged to identify an impact of the detected change on the pipeline artifact(s). The CI/CD pipeline is selectively optimized and executed based on the identified impact to improve the efficiency of the pipeline and the deployment time.