Co-existence of the \u27technical debt\u27 and \u27software legacy\u27 concepts

\u27Technical debt\u27 and \u27software legacy\u27 are concepts that both discuss a state of software that is sub-optimal, time constrained, and explain how this state can decrease an organization\u27s development efficiency. However, there is significant confusion in the way the software engineering community perceive these concepts. In this paper we perform an initial examination of technical debt and software legacy concepts, and examine their somewhat challenging co-existence. In motivating our work, we discuss previous survey results which show that practitioners believe that technical debt largely emerges from software legacy. We then identify sources of confusion in comparing popular definitions of both concepts. Finally, we map the use of the \u27technical debt\u27 and \u27software legacy\u27 concepts in existing research. We conclude that structured co-existence of these terms can be pursued with mutually beneficial gains

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures