Jesus M. Gonzalez-Barahona

Ahmed Zerouali, Tom Mens, Gregorio Robles et al.

Software systems often leverage on open source software libraries to reuse functionalities. Such libraries are readily available through software package managers like npm for JavaScript. Due to the huge amount of packages available in such package distributions, developers often decide to rely on or contribute to a software package based on its popularity. Moreover, it is a common practice for researchers to depend on popularity metrics for data sampling and choosing the right candidates for their studies. However, the meaning of popularity is relative and can be defined and measured in a diversity of ways, that might produce different outcomes even when considered for the same studies. In this paper, we show evidence of how different is the meaning of popularity in software engineering research. Moreover, we empirically analyse the relationship between different software popularity measures. As a case study, for a large dataset of 175k npm packages, we computed and extracted 9 different popularity metrics from three open source tracking systems: libraries.io, npmjs.com and GitHub. We found that indeed popularity can be measured with different unrelated metrics, each metric can be defined within a specific context. This indicates a need for a generic framework that would use a portfolio of popularity metrics drawing from different concepts.

David Moreno-Lumbreras, Gregorio Robles, Daniel Izquierdo-Cortázar et al.

Background/Context: Currently, the usual interface for visualizing data is based on 2-D screens. Recently, devices capable of visualizing data while immersed in VR scenes are becoming common. However, it has not been studied in detail to which extent these devices are suitable for interacting with data visualizations in the specific case of data about software development. Objective/Aim: In this registered report, we propose to answer the following question: "Is comprehension of software development processes, via the visualization of their metrics, better when presented in VR scenes than in 2D screens?" In particular, we will study if answers obtained after interacting with visualizations presented as VR scenes are more or less correct than those obtained from traditional screens, and if it takes more or less time to produce those answers. Method: We will run an experiment with volunteer subjects from several backgrounds. We will have two setups: an on-screen application, and a VR scene. Both will be designed to be as much equivalent as possible in terms of the information they provide. For the former, we use a commercial-grade set of \kibana-based interactive dashboards that stakeholders currently use to get insights. For the latter, we use a set of visualizations similar to those in the on-screen case, prepared to provide the same set of data using the museum metaphor in a VR room. The field of analysis will be related to modern code review, in particular pull request activity. The subjects will try to answer some questions in both setups (some will work first in VR, some on-screen), which will be presented to them in random order. To draw results, we will compare and statistically analyze both the correctness of their answers, and the time spent until they are produced.

Jesus M. Gonzalez-Barahona

Background: During the last years, there has been a lot of discussion and estimations on the energy consumption of Bitcoin miners. However, most of the studies are focused on estimating energy consumption, not in exploring the factors that determine it. Goal: To explore the factors that determine maximum energy consumption of Bitcoin miners. In particular, analyze the limits of energy consumption, and to which extent variations of the factors could produce its reduction. Method: Estimate the overall profit of all Bitcoin miners during a certain period of time, and the costs (including energy) that they face during that time, because of the mining activity. The underlying assumptions is that miners will only consume energy to mine Bitcoin if they have the expectation of profit, and at the same time they are competitive with respect of each other. Therefore, they will operate as a group in the point where profits balance expenditures. Results: We show a basic equation that determines energy consumption based on some specific factors: minting, transaction fees, exchange rate, energy price, and amortization cost. We also define the Amortization Factor, which can be computed for mining devices based on their cost and energy consumption, helps to understand how the cost of equipment influences total energy consumption. Conclusions: The factors driving energy consumption are identified, and from them, some ways in which Bitcoin energy consumption could be reduced are discussed. Some of these ways do not reduce the most important properties of Bitcoin, such as the chances of control of the aggregated hashpower, or the fundamentals of the proof of work mechanism. In general, the methods presented can help to predict energy consumption in different scenarios, based on factors that can be calculated from available data, or assumed in scenarios.