Advancing Sustainability in Software Delivery through Data Technologies A Multi Dimensional Approach

Abstract

This research explores the concept of software sustainability, aiming to ensure the newlineenduring use of software systems in environmentally friendly, resilient, and efficient ways. newlineThe opportunities of leveraging data technologies in improving sustainable software newlinedelivery. The research examines current sustainability approaches and their challenges and newlinegaps, reviewing current research work, practices and models, also the insights from newlineindustry gained by surveys and interviews. A comprehensive software sustainability model newlineis proposed, offering a deployable framework, metrics, and roadmap. The study prioritizes newlineDevOps and software operations experience as key drivers of sustainability, proposing a newlinedata-driven decision-making approach and leveraging machine learning methods to newlineenhance DevOps readiness prediction. Various Machine learning algorithms were analyzed newlineand a novel A* Adaboost algorithm is proposed to improve DevOps readiness newlinepredictability. Moreover, a comprehensive security framework is proposed to enhance newlineDevOps robustness. This framework comprises of a composite approach that leverages newlineauthentication techniques, improved access control, secure data storage , vulnerability newlinemanagement and improvement techniques to increased resilience. Statistical methods and newlinedata analytics are used to determine factors that improve software operations experience, newlineusing feedback data, qualitative studies, and historical support data analysis. Experimental newlineresults highlight the role of data technologies, including machine learning and statistical newlinemethods, in advancing sustainable software development and operations. By combining newlinequalitative and quantitative analysis techniques with the latest technology, the research newlineprovides evidence-based insights, distinguishing itself from existing efforts in the field. newline newline