DApps Ecosystems: Mapping the Network Structure of Smart Contract Interactions

Data availability - The datasets analysed during the current study can be retrieved using the tool presented in Smart contracts data of Dapps are publicly available from their respective Github repositories [https://github.com/DerwenAI/disparity_filter]. To support future work in this area, we have made our dataset publicly available via the Zenodo repository https://zenodo.org/records/12731531 and https://zenodo.org/records/13772792.Preprint available on arxiv - https://doi.org/10.48550/arXiv.2401.01991Decentralized applications (DApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have recently gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each DApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the DApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the system’s robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each DApp play a central role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures.Ethereum foundation grant FY23-104

The emotional side of software developers in JIRA

Issue tracking systems store valuable data for testing hypotheses concerning maintenance, building statistical prediction models and (recently) investigating developer affectiveness. For the latter, issue tracking systems can be mined to explore developers emotions, sentiments and politeness |affects for short. However, research on affect detection in software artefacts is still in its early stage due to the lack of manually validated data and tools. In this paper, we contribute to the research of affects on software artefacts by providing a labeling of emotions present on issue comments. We manually labeled 2,000 issue comments and 4,000 sentences written by developers with emotions such as love, joy, surprise, anger, sadness and fear. Labeled comments and sentences are linked to software artefacts reported in our previously published dataset (containing more than 1K projects, more than 700K issue reports and more than 2 million issue comments). The enriched dataset presented in this paper allows the investigation of the role of affects in software development

A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functions

The preprint version archived on this insttutional repository is available online at: https://arxiv.org/abs/2305.09402 . It has not been certified by peer review.This paper evaluates the capability of two state-of-the-art artificial intelligence (AI) models, GPT-3.5 and Bard, in generating Java code given a function description. We sourced the descriptions from CodingBat.com, a popular online platform that provides practice problems to learn programming. We compared the Java code generated by both models based on correctness, verified through the platform's own test cases. The results indicate clear differences in the capabilities of the two models. GPT-3.5 demonstrated superior performance, generating correct code for approximately 90.6% of the function descriptions, whereas Bard produced correct code for 53.1% of the functions. While both models exhibited strengths and weaknesses, these findings suggest potential avenues for the development and refinement of more advanced AI-assisted code generation tools. The study underlines the potential of AI in automating and supporting aspects of software development, although further research is required to fully realize this potential

A New Monitor and Control Power Supply PCB for Biasing LNAs of Large Radio Telescopes Receivers

The biasing of low noise amplifiers (LNA) is of paramount importance for the receivers of large radio telescopes. High stability, optimal trade-off between gain and noise figure, remote control, and mitigation of the radio frequency interferences (RFIs) are all desirable features in the choice of the electronic board devoted to power supply the LNAs. In this paper, we propose the design and characterization of a multilayer printed circuit board (PCB), named GAIA, able to meet all the aforementioned requirements. The GAIA board is a 3-Unit, four-layer, rack-mountable, programmable PCB for the remote biasing of the LNAs, with monitor and control capabilities, specifically designed to operate in the receivers of the 64-m diameter Sardinia Radio Telescope (SRT). We describe the architecture, layout, and measurements of the GAIA board. Our results show that the GAIA power supply provides high stability of the output bias voltages and, in comparison with the old analogic biasing board used so far in the SRT receivers, it shows comparable or better frequency stability, other than a remarkable mitigation of the RFIs

Space Debris Detection in Low Earth Orbit with the Sardinia Radio Telescope

Space debris are orbiting objects that represent a major threat for space operations. The most used countermeasure to face this threat is, by far, collision avoidance, namely the set of maneuvers that allow to avoid a collision with the space debris. Since collision avoidance is tightly related to the knowledge of the debris state (position and speed), the observation of the orbital debris is the key of the problem. In this work a bistatic radar configuration named BIRALET (BIstatic RAdar for LEO Tracking) is used to detect a set of space debris at 410 MHz, using the Sardinia Radio Telescope as the receiver antenna. The signal-to-noise ratio, the Doppler shift and the frequency spectrum for each debris are reported