An agile-devops reference architecture for teaching enterprise agile

©2019 The authors and IJLTER.ORG. All rights reserved. DevOps emerged as an important extension to support the Agile development for frequent and continuous software delivery. The adoption of Agile-DevOps for large scale enterprise agility depends on the most important human capability such as people competency and experience. Hence, academic education and professional training is key to the successful adoption of Agile-DevOps approach. Thus, education and training providers need to teach Agile-DevOps. However, the challenge is: how to establish and simulate an effective Agile-DevOps technology environment for teaching Enterprise Agile? This paper introduces the integrated Adaptive Enterprise Project Management (AEPM) and DevOps Reference Architecture (DRA) approach for adopting and teaching the Agile-DevOps with the help of a teaching case study from the University of Technology - Sydney (UTS), Australia. These learnings can be utilised by educators to develop and teach practice-oriented Agile-DevOps for software engineering courses. Furthermore, the experience and observations can be employed by researchers and practitioners aiming to integrate Agile-DevOps at the large enterprise scale

From Ad-Hoc Data Analytics to DataOps

The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage

Bridging Disciplines with AI-Powered Coding: Empowering Non-STEM Students to Build Advanced APIs in the Humanities

The integration of AI-powered coding assistants, such as Cursor AI, GitHub Copilot, and Replit’s Ghostwriter AI, represents a transformative shift in programming education, particularly for non-STEM students. These tools democratize coding by enabling natural language code generation, intelligent error correction, and context-aware assistance within familiar coding environments. This article explores how these technologies empower educators across disciplines to introduce basic and advanced coding concepts to humanities students, a demographic traditionally underserved in programming education. By leveraging AI, instructors can teach non-STEM students the foundational principles of coding and guide them through the development of sophisticated projects, such as building APIs for literary analysis or creative world-building. These endeavors, once reserved for advanced digital humanities research, now become accessible within the framework of undergraduate humanities courses. The article examines the practical applications of AI-assisted coding in humanities education, demonstrating how these tools facilitate a deeper engagement with digital methodologies, thus expanding the horizons of what is possible in these fields. Additionally, it discusses the potential for AI-powered assistants to address the unique needs of non-STEM learners, offering a tailored educational experience that aligns with their academic and creative pursuits. This approach not only enriches the humanities curriculum but also fosters interdisciplinary collaboration, preparing students for a future where coding literacy is an essential skill across all domains

Role of AI/ML in decision making in software release management

This thesis explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing decision-making processes in software release management. The research focuses on understanding how AI/ML technologies improve efficiency, accuracy, and risk management while identifying challenges that hinder their integration. A qualitative approach was adopted, including interviews with industry experts to gather insights into the current practices and experiences with AI/ML tools. The findings highlight the benefits of automating repetitive tasks, optimizing resource allocation, and predicting potential risks, leading to faster and more reliable software releases. However, challenges such as data quality, adaptability to evolving environments, and ethical considerations remain significant hurdles. The study concludes that integrating AI/ML offers transformative potential for release management, provided organizations address technical and operational limitations. Key contributions include a better understanding of practical AI/ML applications and recommendations for overcoming challenges to enable effective decision-making in software development

After the success of DevOps introduce DataOps in enterprise culture

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementA lot of organizations implemented DevOps processes with success. This allowed different areas like development, operations, security and quality work together. This cooperation, and processes associated to the work with these areas are producing excellent results. The organizations are developing many applications that support operation and are producing a lot of data. This data has a significant value for organizations because must be used in analysis, reporting and more recently data science projects to support decisions. It is time to take decisions supported in data and for this is necessary to transform organizations in a data-driven organizations and for this we need processes to deal with this data across all teams. This dissertation follows a design science research approach to apply multiple analytical methods and perspectives to create an artifact. The type of evidence within this methodology is a systematic literature review, with the goal to attain insights into the current state-of-the art research of DataOps implementation. Additionally, proven best practices from the industry are examined in depth to further strengthen the credibility. Thereby, the systematic literature review shall be used to pinpoint, analyze, and comprehend the obtainable empirical studies and research questions. This methodology supports the main goal of this dissertation, to develop and propose evidence-based practice guidelines for the DataOps implementation that can be followed by organizations

Mobile DevOps in Education: Practical Training through Application Development

This paper investigates the effectiveness of teaching DevOps concepts to students through practical mobile application development. We pay particular attention to the challenges specific to the mobile environment. These include the complexity of deploying applications in stores such as Google Play and the App Store and the need to ensure constant and convenient interaction with mobile users. The paper presents a pedagogical model combining project-based learning, containerization, automated CI/CD processes, and cloud platforms. We adapted these elements to the specifics of mobile development. The research presents examples of student projects that implement DevOps practices. In particular, it describes the automation of testing on different types of mobile devices, the setup of build and deployment processes, and the monitoring of application performance in a cloud environment. This study conducts a comparative analysis between the traditional approach to training and the DevOps-oriented methodology. The indicators considered include update cycle duration, number of errors, and adaptation to mobile platforms. The results confirmed that using DevOps in mobile development education improves the quality of students’ technical training, promotes the development of their practical skills, and enhances their competitiveness in the field of mobile technologies

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

Science-Technology Division

The Chair\u27s column talking about what is happening within the division and the upcoming 2012 Conference in Chicago