Stuck in Limbo with Magical Solutions: The Testers’ Lived Experiences of Tools and Automation

The automation of people’s roles at work brings changes to their lives and work, bringing advantages of increased effectiveness and efficiency, yet potentially life-changing effects, including redundancy. The software industry’s purpose is to automate people’s tasks and activities, and this applies also to jobs within the software industry, including teams who specialise in testing software. Test automation projects are not always successful, and our research initially set out to discover whether the challenges were usability-related, and whether HCI methods could help improve tools. We discovered a much richer story, which told of emotional stresses and life experiences within the software testing community. We discuss how automation, with all its benefits, affects motivation, causing disassociation of testers from their roles, and affecting their job-task mix. We show reasons why software test automation affects testers. Finally, we set out our position for our research about the li ved experience of software testers using automation, which we are calling TX: The Testers’ Lived Experiences of Tools and Automation, and argue that the effect of automation and tooling on testers’ lived experience and its effect on their motivation is an area of study worthy of research

Computational fluid dynamics modelling of an entire synchronous generator for improved thermal management

This study is the first in a series dedicated to investigating the airflow and thermal management of electrical machines. Owing to the temperature dependent resistive losses in the machine's windings, any improvement in cooling provides a direct reduction in losses and an increase in efficiency. This study focuses on the airflow which is intrinsically linked to the thermal behaviour of the machine as well as the windage power consumed to drive the air through the machine. A full computational fluid dynamics (CFD) model has been used to analyse the airflow around all major components of the machine. Results have been experimentally validated and investigated. At synchronous speed the experimentally tested mass flow rate and windage torque were under predicted by 4% and 7%, respectively, by the CFD. A break-down of torque by component shows that the fan consumes approximately 87% of the windage torque

Data Driven Insight into Fish Behaviour and their use for Precision Aquaculture

Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This rapid expansion has led to the growth of the Precision Aquaculture concept that aims to exploit data-driven management of fish production, thereby improving the farmer's ability to monitor, control, and document biological processes in farms. Fundamental to those is monitoring of environmental and animal processes within a cage, and processing those data towards farm insight using models and analytics. This paper presents an analysis of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada. Information on fish behaviour were collected using hydroacoustic sensors that sampled the vertical distribution of fish in a cage at high spatial and temporal resolution, while a network of environmental sensors characterised local site conditions. We present an analysis of the hydroacoustic datasets using AutoML (or automatic machine learning) tools that enables developers with limited machine learning expertise to train high-quality models specific to the data at hand. We demonstrate how AutoML pipelines can be readily applied to aquaculture datasets to interrogate the data and quantify the primary features that explains data variance. Results demonstrate that variables such as temperature, wind conditions, and hour-of-day were important drivers at all sites. Further, there were distinct differences in factors that influenced local variations driven by factors such as water depth and ambient environmental conditions (particularly dissolved oxygen). The framework offers a transferable approach to interrogate fish behaviour within farm systems, and quantify differences between sites

Measuring the Neutrino Cross Section Using 8 years of Upgoing Muon Neutrinos Observed with IceCube

The IceCube Neutrino Observatory detects neutrinos at energies orders of magnitude higher than those available to current accelerators. Above 40 TeV, neutrinos traveling through the Earth will be absorbed as they interact via charged current interactions with nuclei, creating a deficit of Earth-crossing neutrinos detected at IceCube. The previous published results showed the cross section to be consistent with Standard Model predictions for 1 year of IceCube data. We present a new analysis that uses 8 years of IceCube data to fit the ν$_{μ}$ absorption in the Earth, with statistics an order of magnitude better than previous analyses, and with an improved treatment of systematic uncertainties. It will measure the cross section in three energy bins that span the range 1 TeV to 100 PeV. We will present Monte Carlo studies that demonstrate its sensitivity

Non-standard neutrino interactions in IceCube

Non-standard neutrino interactions (NSI) may arise in various types of new physics. Their existence would change the potential that atmospheric neutrinos encounter when traversing Earth matter and hence alter their oscillation behavior. This imprint on coherent neutrino forward scattering can be probed using high-statistics neutrino experiments such as IceCube and its low-energy extension, DeepCore. Both provide extensive data samples that include all neutrino flavors, with oscillation baselines between tens of kilometers and the diameter of the Earth. DeepCore event energies reach from a few GeV up to the order of 100 GeV - which marks the lower threshold for higher energy IceCube atmospheric samples, ranging up to 10 TeV. In DeepCore data, the large sample size and energy range allow us to consider not only flavor-violating and flavor-nonuniversal NSI in the μ−τ sector, but also those involving electron flavor. The effective parameterization used in our analyses is independent of the underlying model and the new physics mass scale. In this way, competitive limits on several NSI parameters have been set in the past. The 8 years of data available now result in significantly improved sensitivities. This improvement stems not only from the increase in statistics but also from substantial improvement in the treatment of systematic uncertainties, background rejection and event reconstruction

IceCube Search for Earth-traversing ultra-high energy Neutrinos

The search for ultra-high energy neutrinos is more than half a century old. While the hunt for these neutrinos has led to major leaps in neutrino physics, including the detection of astrophysical neutrinos, neutrinos at the EeV energy scale remain undetected. Proposed strategies for the future have mostly been focused on direct detection of the first neutrino interaction, or the decay shower of the resulting charged particle. Here we present an analysis that uses, for the first time, an indirect detection strategy for EeV neutrinos. We focus on tau neutrinos that have traversed Earth, and show that they reach the IceCube detector, unabsorbed, at energies greater than 100 TeV for most trajectories. This opens up the search for ultra-high energy neutrinos to the entire sky. We use ten years of IceCube data to perform an analysis that looks for secondary neutrinos in the northern sky, and highlight the promise such a strategy can have in the next generation of experiments when combined with direct detection techniques