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Journal Article Open Access Software Engineering

Cultural Transformation in DevOps Adoption: Organizational Change Management, Psychological Safety, and the Role of Leadership in Enabling Engineering Culture Shifts

The dominant discourse in DevOps adoption research has centered on toolchain selection and process automation, while the organizational and psychological dimensions of transformation have received comparatively little rigorous attention. This paper addresses that gap through a qualitative study of DevOps cultural transformation in eight organizations, drawing on 76 interviews with practitioners ranging from individual contributors to C-suite executives, supplemented by participant observation at four of the organizations over a six-month period. Grounded in organizational change theory and psychological safety research, our analysis identifies three primary cultural transformation pathways — Top-Down Mandate, Grassroots Emergence, and Centre of Excellence Diffusion — and characterizes the conditions under which each succeeds or fails. We find that psychological safety, as operationalized by Edmondson (1999), is the single most predictive cultural variable for DevOps transformation success, outperforming technology budget, leadership commitment, and prior agile experience in our qualitative comparative analysis. Transformations that explicitly cultivated psychological safety through blameless postmortems, open deployment failure communication, and junior engineer empowerment reached self-sustaining cultural momentum 2.4 times faster than those relying on mandate-only approaches. We develop a DevOps Culture Transformation Playbook comprising 11 leadership interventions mapped to transformation phase and organizational context.

Ngozi Anozie, Sebastian Fischer, Mi-Young Choi, David Osei-Poku· Dec 2016· 341 citations
Journal Article Subscription Robotics

Simultaneous Localization and Mapping for Autonomous Mobile Robots in Dynamic Indoor Environments: Comparison of EKF-SLAM, Particle Filter SLAM, and Graph-Based SLAM Under Moving Obstacle Conditions

Simultaneous Localization and Mapping (SLAM) algorithms underpin autonomous navigation in mobile robots operating in indoor environments, yet the assumption of a static world that governs most published SLAM formulations is systematically violated in real deployment environments -- warehouses, hospitals, and offices -- where humans, vehicles, and other robots constitute dynamic obstacles. This paper presents a rigorous comparative evaluation of three SLAM algorithm families -- Extended Kalman Filter SLAM (EKF-SLAM), Rao-Blackwellized Particle Filter SLAM (RBPF-SLAM), and Graph-Based SLAM with g2o optimization -- under controlled dynamic obstacle conditions using a standardized evaluation platform comprising a TurtleBot3 robot in a repeatable 200 square meter indoor test environment with programmable dynamic obstacle injection. We parameterize dynamic obstacle density from 0 to 40 percent of navigable space and measure localization RMSE, map consistency score, computational load, and loop closure success rate as primary evaluation dimensions. Graph-Based SLAM with dynamic object masking achieves the lowest localization RMSE (4.2 cm mean) under high obstacle density conditions, while RBPF-SLAM demonstrates the fastest recovery from localization failures (mean 8.4 seconds). We introduce the Dynamic SLAM Robustness Index (DSRI) combining localization accuracy, failure recovery speed, and computational efficiency, and provide algorithm selection guidance for six common deployment scenario profiles. All evaluation code, datasets, and robot configurations are released as an open benchmarking suite.

Adaobi Okoye, Jonas Holm, Takeshi Mori, Fatima Benali· Dec 2016· 287 citations