Containers in Software Development: A Systematic Mapping Study
TL;DR
Containers are most often discussed in the context of cloud computing, performance and DevOps, and it is found that what is currently missing is more deeply focused research.
TL;DR
Containers are most often discussed in the context of cloud computing, performance and DevOps, and it is found that what is currently missing is more deeply focused research.
In this project, one will be working on building a Continuous Integration and Continuous Deployment pipeline using Microsoft Azure to automate software delivery and improve overall efficiency.
This paper presents a realistic case study showing how the implementation of DevOps principles in cross-functional teams, with the help of the standardized pipelines and integrated automation, facilitates cooperation, reduces cycle times, enhances quality assurance and accelerates delivery outcomes.
This paper analyzes the two software architectural approaches namely Monolithic Architecture vs Microservices Architecture when considering scalable software development, and puts the following into consideration: empirical trade-offs, scalability consideration, organizational implications, and development complexity.
A systematic mapping study of Green SE research published between 2010 and 2024 indicates that SE conferences host the majority of energy-related literature, and optimization and benchmarking emerged as the most prevalent research types.
In the context of rapid digitalisation and a high level of heterogeneity of contemporary corporate ecosystems, the problem of fragmentation of the information space, and the emergence of isolated data warehouses becomes a critical barrier to business efficiency. The purpose of the study was to develop and substantiate a comprehensive methodology for automating business processes of an enterprise based on the application programming interface – integration of the application programming interface, combining the choice of optimal architectural patterns with mechanisms for secure data exchange. To achieve this goal, the researchers applied system analysis of architectural styles, prototyping of software gateways in Python, and computer modelling of load scenarios in an isolated virtual environment using Docker containers and the Locust tool. The study involved a comparative analysis of the Representational State Transfer, Simple Object Access Protocol, and Graph Query Language protocols in terms of performance and network load, which revealed significant traffic overhead and delays of up to 185 ms when using outdated standards. A hybrid integration methodology was proposed that combines the Representation State Transfer architecture for mobile clients and Graph Query Language for web interfaces, which reduced the amount of transmitted data by 73% and reduced the system response time to 65 ms. A secure data exchange algorithm has been developed based on the use of an intermediate layer (middleware) and a security gateway (API Gateway), which implement two-level request validation and centralised access control to neutralise the risks of unauthorised interference. The expediency of using the "Strangler Fig" migration pattern was substantiated, which allowed for a gradual transition from monolithic enterprise resource planning systems to microservice architecture through a specialised Python gateway without stopping operational activities. It has been experimentally proven that the introduction of asynchronous message queues allows the system to maintain stable operation within 320 ms even at peak loads of up to 5,000 requests per second, in contrast to monolithic solutions that demonstrate exponential performance degradation
This paper explores the integration of Spring Boot with containerization and observability, focusing on best practices, architectural patterns, and challenges, and delves into containerization strategies using Docker and orchestration with Kubernetes, emphasizing their roles in deployment and scalability.
Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research.
Human-Computer Interaction and Visualization
Human-Computer Interaction and Visualization
An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra.
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