相关论文: Harmonizing DevOps Taxonomies -- Theory Operationa…

DevSecOps is a software development paradigm that places a high emphasis on the culture of collaboration between developers (Dev), security (Sec) and operations (Ops) teams to deliver secure software continuously and rapidly. Adopting this…

Introduction of DevOps into the software development life cycle represents a cultural shift in the IT culture, amalgamating development and operations to improve delivery speed in a rapid and maintainable manner. At the same time, security…

Context: Several companies are migrating their information systems into the Cloud. Microservices and DevOps are two of the most common adopted technologies. However, there is still a lack of understanding how to adopt a microservice-based…

Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing…

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust…

Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different…

DevOps and Artificial Intelligence (AI) are interconnected with each other. DevOps is a business-driven approach to providing quickly delivered quality software, and AI is the technology that can be used in the system to enhance its…

Communication between practitioners is essential for product quality in the DevOps context. This communication often takes place through deployment diagrams of a system under development. However, it is common diagrams to become ambiguous…

Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to…

In recent years, DevOps, the unification of development and operation workflows, has become a trend for the industrial software development lifecycle. Security activities turned into an essential field of application for DevOps principles…

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and…

Gamification is the use of game elements such as points, leaderboards, and badges in a non-game context to encourage a desired behavior from individuals interacting with an environment. Recently, gamification has found its way into software…

Context: Applying Microservices Architecture (MSA) in DevOps has received significant attention in recent years. However, there exists no comprehensive review of the state of research on this topic. Objective: This work aims to…

We outline a comprehensive framework for artificial intelligence (AI) Application Operations (AIAppOps), based on real-world experiences from diverse organizations. Data-driven projects pose additional challenges to organizations due to…

Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of discovery. Various scientific disciplines, including…

As the use of DevOps practices continues to grow, organizations are seeking ways to improve collaboration, speed up development cycles, and increase security, transparency, and traceability. Blockchain technology has the potential to…

DevOps is the contemporary term for a software development culture that purposefully blurs distinction between software development and IT operations by treating "infrastructure as code." DevOps teams typically implement practices…

IT systems of today are becoming larger and more complex, rendering their human supervision more difficult. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to AI and…

Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown. Different from pure code generation, handling the DevOps…

Knowledge about the roles developers play in a software project is crucial to understanding the project's collaborative dynamics. Developers are often classified according to the dichotomy of core and peripheral roles. Typically,…