A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment

Software and information systems have become a core competency for every business in this connected world. Any enhancement in software delivery and operations will tremendously impact businesses and society. Sustainable software development is one of the key focus areas for software organizations. The application of intelligent automation leveraging artificial intelligence and cloud computing to deliver continuous value from software is in its nascent stage across the industry and is evolving rapidly. The advent of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organizations have adopted DevOps to develop and operate their software systems and improve efficiency. Software organizations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organizations is beset with multiple challenges. These issues can be overcome by understanding and structurally addressing the pain points. This paper presents the preliminary analysis of the interviews with the relevant stakeholders. Ground truths were established and applied to evaluate various machine learning algorithms to compare their accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the adoption of DevOps and the contexts in which the DevOps practices are viable. The experimental results will show that machine learning can predict an organization's readiness to adopt DevOps

Slide-block: End-to-end amplified security to improve DevOps resilience through pattern-based authentication

DevOps represents the fusion of cultural philosophies, tools, and practices that rapidly enhance an organization's capacity to deploy services and applications. Cloud-based tools, a subset of DevOps services, facilitate collaboration between development and operations teams within an organization. However, persistent challenges such as inadequate security management, substantial leakage of sensitive data, and system/service unavailability pose significant threats to sustainability. We propose an end-to-end enhanced security framework to fortify DevOps resilience by implementing authentication and vulnerability management through the Slide-Block methodology. Our approach comprises four sequential processes: pattern-based authentication, tri-level access control, privacy-focused data storage, and vulnerability management and correction. Initially, we establish candidate legitimacy through pattern-based authentication using the Magnificent Chacha-Poly 1305 algorithm. Subsequently, we devise effective access policies using the Enhanced Deep Deterministic Policy Gradient (EDDPG) algorithm, employing tri-level access control based on trust value, attributes, and roles for optimal user and developer selection via the African Vulture Optimization Algorithm (AVOA). Moreover, we encrypt data in transit and at rest using Mcha-Poly 1305, considering sensitivity, and store it in a blockchain to enhance data privacy. Our approach incorporates a sliding window blockchain for secure data transmission and storage. Finally, we identify and address attack and application-based issues using the Tweak Naive Bayes (Tweak-NB) algorithm and Intruder Vulnerability Scanner (IVS). Our Slide-Block framework demonstrates superior performance in detection rate, authentication time, packet loss, security strengthening, communication overhead, and latency compared to existing models

Data_Sheet_1_A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment.docx

Software and information systems have become a core competency for every business in this connected world. Any enhancement in software delivery and operations will tremendously impact businesses and society. Sustainable software development is one of the key focus areas for software organizations. The application of intelligent automation leveraging artificial intelligence and cloud computing to deliver continuous value from software is in its nascent stage across the industry and is evolving rapidly. The advent of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organizations have adopted DevOps to develop and operate their software systems and improve efficiency. Software organizations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organizations is beset with multiple challenges. These issues can be overcome by understanding and structurally addressing the pain points. This paper presents the preliminary analysis of the interviews with the relevant stakeholders. Ground truths were established and applied to evaluate various machine learning algorithms to compare their accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the adoption of DevOps and the contexts in which the DevOps practices are viable. The experimental results will show that machine learning can predict an organization's readiness to adopt DevOps.</p