Related papers: DevOps and its Philosophy : Education Matters!
Machine Learning Operations (MLOps) is becoming a highly crucial part of businesses looking to capitalize on the benefits of AI and ML models. This research presents a detailed review of MLOps, its benefits, difficulties, evolutions, and…
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations. AIOps,…
To improve the quality of programs we provide an approach to guidance in the process of program development. At the higher level the various activities and their dependencies to structure the process are identified. At the lower level,…
Due to their characteristics, millennials prefer learning-by-doing and social learning, such as project-based learning. However, software development projects require not only technical skills but also creativity; Design Thinking can serve…
The intersection between security and continuous software engineering has been of great interest since the early years of the agile development movement, and it remains relevant as software development processes are more frequently guided…
Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn…
Agile methods provide an organization or a team the flexibility to adopt a selected subset of principles and practices based on their culture, their values, and the types of systems that they develop. More specifically, every organization…
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and…
Most university curricula consider software processes to be on the fringes of software engineering (SE). Students are told there exists a plethora of software processes ranging from RUP over V-shaped processes to agile methods. Furthermore,…
Modern software systems require various capabilities to meet architectural and operational demands, such as the ability to scale automatically and recover from sudden failures. Self-adaptive software systems have emerged as a critical focus…
The use of agile principles and practices in software development is becoming a powerful force in today's workplace. In our quest to develop better products, therefore, it is imperative that we strive to learn and understand the application…
Software Engineering is the process of a systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted…
This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering…
Programming and software engineering courses in computer science curricula typically focus on both providing theoretical knowledge of programming languages and best-practices, and developing practical development skills. In a massive course…
Delivering hands-on practice laboratories for introductory courses on operating systems is a difficult task. One of the main sources of the difficulty is the sheer size and complexity of the operating systems software. Consequently, some of…
DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm has gained prominence, urging practitioners to incorporate…
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail…
Researchers have been highly active to investigate the classical machine learning workflow and integrate best practices from the software engineering lifecycle. However, deep learning exhibits deviations that are not yet covered in this…
For Model-Driven Engineering (MDE) to become Agile, it is has to be usable with and for DevOps as the technical basis of Agility. We describe our experiences in implementing and applying the BB8 architecture that provides a means to reuse…
The domain of cyber-physical systems (CPS) has recently seen strong growth, e.g., due to the rise of the Internet of Things (IoT) in industrial domains, commonly referred to as "Industry 4.0". However, CPS challenges like the strong…