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：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.

公开(公告)号：GB2600554A

公开(公告)日：2022-05-04

申请号：GB202113987

申请日：2021-09-30

Applicant: IBM

Inventor： LIN CHEN ,  JINHO HWANG ,  MUHAMMAD FAITH BULUT ,  ALI KANSO ,  SHRIPAD NADGOWDA

IPC: G06F8/72

Abstract: A computer system wherein there is a genetic manager to apply a genetic algorithm to parent re-factoring operations selected from an initial identified set of such operations for source code to produce an offspring population of the operations as a subset of the parent re-factoring operations. A score manager measures a fitness score of each re-factoring operation including collecting runtime traces of the source code and applies the traces to the subset such that a classifier can prioritise operations within the subset based on a corresponding fitness score. Responsive to the prioritisation one or more of the re-factoring operations can be applied to the source code to produce one or more micro-service candidates. A crossover operator may be applied to generate a combination of offspring re-factoring operations, and a mutation operator may be applied to introduce variability to the generated combination. The application of the genetic algorithm and fitness score measurement may be an iterative process wherein an operation is selected as the next parent re-factoring operation on an objective factor and iteratively applied to the process.