The emerging paradigm of 6G wireless communication networks envisions ultra-reliable low-latency communication (URLLC), massive machine-type communications (mMTC), and pervasive edge computing intelligence. In heterogeneous mobile edge computing (MEC) networks, dynamically offloading compute-intensive tasks (e.g., augmented reality rendering, connected vehicular telemetry, autonomous robotic control) while orchestrating multi-tenant network slicing under time-varying channel conditions is an NP-hard stochastic optimization problem. Centralized reinforcement learning algorithms suffer from extreme communication overhead, severe backhaul congestion, and severe privacy vulnerabilities. Conversely, standard synchronous Federated Learning (FL) methods encounter severe 'straggler effects' caused by heterogeneous edge device processing capabilities. In this paper, we propose AF-EdgeRL, a novel Byzantine-resilient Asynchronous Federated Reinforcement Learning framework tailored for distributed resource allocation and dynamic task offloading. AF-EdgeRL deploys a distributed Proximal Policy Optimization (PPO) agent across edge servers and end-user devices, combined with a Staleness-Aware Adaptive Weight Aggregator (SAWA) that dynamically adjusts model update gradients based on hardware compute latency and channel state information (CSI). Furthermore, we establish theoretical convergence guarantees under non-convex reinforcement learning objectives. Evaluated on a high-fidelity 6G MEC simulator with real-world mobile mobility traces (Telecom Italia Milano dataset), AF-EdgeRL reduces end-to-end task execution latency by 41.2%, achieves 99.999% URLLC deadline compliance, and decreases edge energy consumption by 32.6% compared to state-of-the-art synchronous FedRL and centralized DRL baselines.
Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 728 citations· ⚡54
Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 3, 2026
