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Search across 2.4 million peer-reviewed documents from journals, conferences, and standards.

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Journal Article Open Access Software Engineering

Trunk-Based Development at Scale: Empirical Analysis of Branching Strategy Impact on Integration Frequency, Merge Conflict Rate, and Delivery Performance

Branching strategy is a foundational DevOps decision that determines the cadence of integration, the severity of merge conflicts, and the attainability of continuous integration as defined by its original proponents. Despite the strong advocacy in practitioner literature for trunk-based development (TBD) over long-lived branch strategies such as GitFlow, rigorous empirical evidence comparing their delivery performance consequences has been limited. This paper addresses this gap through a large-scale empirical study of 30 engineering organizations across six industries, combining pipeline telemetry analysis with practitioner surveys (n=529) and structured interviews. We operationalize six delivery performance metrics for comparison: integration frequency, merge conflict rate, build failure rate, time to green build, deployment frequency, and change failure rate. Organizations practicing TBD with feature flags exhibit statistically significantly higher integration frequency (mean 14.2 integrations per developer per week vs 1.8 for GitFlow), lower merge conflict rates (0.12 vs 0.89 conflicts per pull request), and 48% higher deployment frequency. Subgroup analysis reveals that TBD benefits are amplified in teams larger than 8 engineers and in systems with more than 40 microservices. We also identify four TBD adoption prerequisites — feature flag infrastructure, fast build pipelines, comprehensive automated test suites, and code review tooling maturity — and provide a readiness assessment instrument.

Chukwudi Eze, Anna Lindqvist, Hiroki Yamamoto, Catarina Sousa· Oct 2021· 421 citations
Journal Article Subscription Internet of Things

Digital Twin Architectures for Industrial IoT: Real-Time Synchronization, Predictive Maintenance Modeling, and Cybersecurity Threat Surfaces in Manufacturing Environments

Digital twins -- real-time virtual representations of physical assets synchronized through continuous IoT sensor data streams -- have emerged as a transformative paradigm in industrial manufacturing, enabling predictive maintenance, process optimization, and remote operations at a fidelity previously unattainable. This paper presents a comprehensive technical framework for industrial IoT digital twin architecture, addressing three interrelated engineering challenges: real-time synchronization fidelity under constrained industrial network conditions, predictive maintenance model accuracy for rotating machinery, and cybersecurity threat surface analysis specific to digital twin deployments. We evaluate three synchronization architectures -- push-based MQTT streaming, pull-based REST polling, and event-sourced CQRS -- across five industrial network scenarios including OPC-UA over Ethernet, PROFINET, and 4G LTE-M backhaul, measuring synchronization latency, data completeness, and bandwidth efficiency. For predictive maintenance, we compare physics-informed neural networks against purely data-driven LSTM models for bearing failure prediction, finding that physics-informed approaches achieve 94.1% prediction accuracy at 72-hour forecast horizons versus 87.3% for LSTM, with significantly lower training data requirements. The cybersecurity analysis identifies 14 attack vectors specific to digital twin architectures, including twin poisoning through sensor data spoofing and model inversion attacks targeting physical process parameters, and proposes a Digital Twin Security Framework with mitigation strategies for each.

Ifenna Okwu, Hanna Larsson, Ryo Inoue, Laila Hassan· Oct 2021· 367 citations