IMPLEMENTASI CONTIONOUS INTEGRATION DAN CONTINOUS DEPLOYMENT PADA APLIKASI LEARNING MANAGEMENT SYSTEM DI PT. MILLENNIA SOLUSI INFORMATIKA

Aplikasi Learning Management System atau LMS merupakan produk aplikasi yang dikembangkan oleh PT. Millennia Solusi Informatika. Aplikasi LMS ini telah digunakan oleh salah satu jaringan sekolah swasta. Dalam pengembangannya, aplikasi ini menggunakan metode scrum dimana pendekatan metode ini bersifat agile dan dapat menyesuaikan kebutuhan dengan cepat. Berangkat dari hal tersebut maka dalam proses delivery perangkat lunak ini maka perlu menggunakan konsep continuous integration dan continuous deployment guna memenuhi alur pengembangan yang bersifat agile dan dapat berulang. Continous Integration (CI) adalah pengintegrasian kode ke dalam repositori kode kemudian menjalankan penggunaan secara otomatis, cepat dan sering. Sementara Continuous Deployment atau Continuous Delivery (CD) adalah praktik yang dilakukan setelah proses CI selesai dan seluruh kode berhasil terintegrasi, sehingga aplikasi bisa dibangun dan dirilis secara otomatis. Dengan menggunakan metode CI/CD diharapkan dalam proses penyampaian aplikasi dapat terus berlangsung otomatis, cepat dan sering walaupun aplikasi tersebut sudah digunakan oleh pengguna.

Support for Computing in Distributed Environments Based on Continuous Integration

Nowadays, tools for designing scientific applications often do not implement the required continuous integration capabilities of the applied software. Therefore, such overheads as the application development time and experiment execution makespan are substantially increased. In this regard, we propose a new approach to developing scientific applications and carrying out experiments with them. It is based on applying continuous integration to both the applied and system software in developing distributed applied software packages with a modular architecture using the Orlando Tools framework. Within the proposed approach, we provide integrating the Orlando Tools subsystems with the GitLab system and automating the development of package modules. At the same time, Orlando Tools fully support constructing and testing problem-solving schemes (workflows) that combine package modules located on environment resources with different computational characteristics. To this end, Orlando Tools provides the necessary configuring and setting up of computational resources. The practical significance of our study is substantial reduction overheads needed to experiment fulfillments and increase of the resource use efficiency.

SecDocker: Hardening the Continuous Integration Workflow

Current Continuous Integration (CI) processes face significant intrinsic cybersecurity challenges. The idea is not only to solve and test formal or regulatory security requirements of source code but also to adhere to the same principles to the CI pipeline itself. This paper presents an overview of current security issues in CI workflow. It designs, develops, and deploys a new tool for the secure deployment of a container-based CI pipeline flow without slowing down release cycles. The tool, called SecDocker for its Docker-based approach, is publicly available in GitHub. It implements a transparent application firewall based on a configuration mechanism avoiding issues in the CI workflow associated with intended or unintended container configurations. Integrated with other DevOps Engineers tools, it provides feedback from only those scenarios that match specific patterns, addressing future container security issues.

Continuous Integration for Machine Learning Experiments Reproducibility: a Practical Study

The development of a Machine Learning (ML) model depends on many variables in its training. Both model architecture-related variables, such as initial weights and hyperparameters, and general variables, like datasets and framework versions, might impact model metrics and experiment reproducibility. An application cannot be trustworthy if it produces good results only in a specific environment. Therefore, in order to avoid reproducibility issues, some good practices need to be adopted. This paper aims to report a practical experience in developing a machine learning application adopting a workflow that assures the reproducibility of the experiments and, consequently, its reliability, improving the team productivity.