Documentation comments are essential for maintainability, yet they are often missing or outdated. This is true not only for programs in general-purpose languages, but also for artifacts in other languages often found in software projects such as scripts or configuration files. To address this problem, we present an approach that uses Large Language Models (LLMs) to generate header comments (aka, ‘block comments’ or ‘doc-strings’) for elements of different languages in different documentation formats. Given a file in some language and a description of elements in the file to be documented and the documentation format to be used, the approach generates header comments for all undocumented elements in the file that is guaranteed to conform to the documentation format. We describe a prototype implementation and its integration into an industrial development pipeline. Feedback from our industrial partner, an LLM-as-judge evaluation, and the participants of a user study involving a broad range of languages indicates that the approach is viable, able to produce sufficiently high-quality documentation in general, and holds potential for improving industrial documentation practices across different programming languages and teams.

In the face of growing demands for high-quality digital media, enhancing video streaming quality remains a significant challenge, particularly in environments with diverse internet connectivity and limited bandwidth. This paper proposes the Edge-enhanced Streaming (EES) scheme, which leverages edge computing and machine learning to upscale low-bitrate video frames to higher resolutions. Utilizing the distributed computational power of edge devices such as smartphones and laptops, our methodology involves segmenting each video frame into smaller sub-frames. These sub-frames are then processed using a Super-Resolution (SR) machine learning model across available edge devices within the network. This approach optimizes underutilized computational resources, improves processing times, and reduces energy consumption, making it a highly suitable approach for real-time video streaming applications. Furthermore, to address the challenge of device reliability, we incorporate a task replication strategy, ensuring consistent quality improvements even with potential fluctuations in device availability. We evaluate our proposed scheme using the PRIM dataset from the PIRM-SR Challenge. Extensive simulations demonstrate significant enhancements in video quality, confirming the effectiveness of our distributed SR technique in overcoming bandwidth constraints and improving user experience.

Edge computing can satisfy the requirements of latency-critical and data-intensive applications by exploiting com-putational resources of end devices. However, such devices inherently suffer from dynamic user behavior, cyclic task-switching, varying link qualities which often impact their reliability. In addition, in incentivized systems, they may often over-estimate their advertised capabilities and consequently fail on delivering. In this paper, we propose the Reputation-based Task Assignment and Replication (RTAR) scheme. RTAR is the first scheme that uses a black box approach to perform cost-efficient task replication that accounts for workers' reliability and preserves workers' privacy by not requiring or soliciting any information about their devices. RTAR incorporates a reputation model using beta distribution to estimate the worker's reputation based on past performance. We formulate the problem as an Integer Linear Program (ILP) that strives to maximize the overall reputation of recruited workers, while abiding by a certain budget limit for each task. We also propose the RTAR-Heuristic (RTAR-H) scheme. RTAR-H uses matching theory to solve the optimization problem in a time-efficient manner. Extensive evaluations show that RTAR yields 63% and 68% reduction in recruitment cost and number of replicas, respectively, compared to a baseline scheme that blindly maximizes the number of replicas. Moreover, RTAR-H closely approaches the optimal solution, rendering a small gap of up to 1% and 1.2% in terms of task drop rate and recruitment cost, respectively.

IoT systems are advancing to enable higher levels of engagement and omnipresence. A critical yet uncharted domain lies in communicating affect across participants, especially in medical settings where emotions and expressions are pivotal, and eXtended Reality (XR) systems that rely on immersive virtualization of the participating parties. However, IoT systems seldom have the bandwidth or reliability to enable such services. In this demo, we present an experiment that leverages Edge Intelligence and Artificial Intelligence to extract and encode emotions at one edge, and communicate a low-footprint encapsulation of such emotions at the other edge. The proposed architecture is designed to reduce overall traffic and build on low-power video and display equipment, to realize Affective Semantic Communication (AffSeC). This demonstration shall represent AffSeC in a medical setting, where a patient interacts with a physician over a low-BW E2E route. The proposed scheme will be contrasted to standard video compression to demonstrate the efficacy and promise of this model.

Affective computing is an emerging field that aims to develop technologies capable of recognizing and responding to human emotions. However, during communication sessions, the exchange of a high volume of data can cause high latency. One approach to mitigating this issue is semantic communication, which may reduce the amount of data exchanged. Hereby, we propose a novel idea that utilizes semantic communication in affective computing by minimizing the amount of information exchanged between endpoints. Specifically, we examine a use case of a remote doctor application, where a patient’s emotions are captured, and their vital signs are obtained using wearable devices, with this information reported to a remote doctor. To reduce data exchange, we utilize semantic communication to extract the meaning of the conveyed information, rather than transmitting the raw information itself. This approach can enhance the efficiency of communication in URLLC applications and has the potential to improve patient outcomes.

Edge Computing (EC) has been evolving towards harvesting latent yet underutilized computational resources of the Extreme Edge Devices (EEDs), such as autonomous vehicles, smartphones, and tablets. However, EEDs tend to be user-owned devices. This triggers a high level of uncertainty, the impact of which is mostly overlooked. Such uncertainty can stem from the potential loss of network connectivity, battery depletion, as well as the dynamic user access behavior that can affect the computational capability of EEDs and compromise the convenience of users. This uncertainty can profoundly impact the devices' reliability of executing the offloaded tasks. In this context, we propose the Replica Maximization at the Extreme Edge (RMEE) scheme. RMEE employs task replication to achieve maximum reliability and improve successful task execution while abiding by certain QoS requirements. Towards that end, RMEE aims to maximize the number of offloaded replicas for each task, while ensuring that the task execution delay is kept within a certain threshold. We formulate the task replication optimization problem as a Mixed-Integer Linear Program (MILP) and devise an analytical solution using the Karush-Kuhn-Tucker (KKT) conditions and Lagrangian analysis. Extensive simulations have shown that RMEE outperforms other baseline schemes that involve single and fixed number of replicas, in terms of drop rate, satisfaction ratio, and the number of replicas by up to 100%, 100% and 60%, and 95.1 % and 85.4%, respectively.

Edge Computing (EC) is a promising computing paradigm that can foster a wide spectrum of delay-sensitive and/or data-intensive applications. As opposed to cloud computing, which relies on remote cloud servers, EC brings the computing service closer to the end-users, which can significantly reduce the delay. The concept of EC has recently expanded to include harvesting the computation resources of the Extreme Edge Devices (EEDs), such as smartphones, autonomous vehicles, tablets, etc. However, the cost of recruiting EEDs for resource allocation in such EC environments is mostly overlooked. In this paper, we propose the Price-based Compute Clusters Recruitment (PCCR) scheme. In PCCR, we minimize the cost of recruiting the EEDs required to perform a given set of tasks, where each task is satisfied by the collaborative effort of a group of EEDs forming a compute cluster. PCCR strives to minimize the total recruitment cost while keeping the delay below a certain threshold by forming the optimal set of compute clusters from a pool of heterogeneous EEDs available in a given geographical area. We formulate the optimization problem as a Mixed Integer Quadratically Constrained Quadratic Program (MIQCQP). We then derive an analytical solution using the KKT conditions and Lagrangian analysis. Extensive simulations show that PCCR significantly outperforms a prominent baseline approach in terms of recruitment cost.

Edge networks provide ample resources for low-latency service recruitment, unlike remote resources in the Cloud. As such, smart devices and Internet of Things (IoT) nodes form a pool of Extreme Edge Devices (EED) that are within reach of Mist and Fog networks, providing significant advantages in latency, geographic cognizance, and reduced communication costs. EEDs are often recruited in Edge networks assuming they are reliable in their commitment to tasks. However, many EEDs may fail to fulfill their tasks because they operate under opportunistic approaches and are prone to intermittent connectivity. To ameliorate task failure, we aim to optimize task allocation under the assumption of failure. Additionally, we optimize CPU utilization to engage reliable EEDs, resorting to replication when needed to exceed a tunable reliability margin. We demonstrate the efficacy of our model in multiple scenarios and present future work in EED utilization.