This research conducted a systematic review of the literature on machine learning (ML)-
based methods in the context of Continuous Integration (CI) over the past 22 years. The …

Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable
insights in specific phases. The rise of continuous practices in software projects emphasizes …

Continuous integration (CI) has been a de facto standard for building industrial-strength
software. Yet, there is little attention towards applying CI to the development of machine …

Background: Machine Learning (ML) methods are being increasingly used for automating
different activities, eg, Test Case Prioritization (TCP), of Continuous Integration (CI) …

Objectives: This research aims to find the suitable machine learning algorithm for CI system
and investigate the feasibility of creating self-learning test machinery. This goal is achieved …

In every industry, machine learning applications are becoming popular, however, compared
to traditional software applications to the process of developing, deploying, and continuously …

Continuous integration is an indispensable step of modern software engineering practices to
systematically manage the life cycles of system development. Developing a machine …

We recently outlined the vision of" Learning Everywhere" which captures the possibility and
impact of how learning methods and traditional HPC methods can be coupled together. A …

Continuous Integration (CI) is a widely adopted practice for faster code change integration
and testing. Developers often migrate between CI systems in pursuit of features like matrix …

Continuous Integration (CI) systems aim to provide quick feedback on the success of the
code changes by keeping on building the entire systems upon code changes are committed …