Abstract
This paper introduces the concept of energy debt: a new metric, reflecting the implied cost in terms of energy consumption over time, of choosing a flawed implementation of a software system rather than a more robust, yet possibly time consuming, approach. A flawed implementation is considered to contain code smells, known to have a negative influence on the energy consumption. Similar to technical debt, if energy debt is not properly addressed, it can accumulate an energy "interest". This interest will keep increasing as new versions of the software are released, and eventually reach a point where the interest will be higher than the initial energy debt. Addressing the issues/smells at such a point can remove energy debt, at the cost of having already consumed a significant amount of energy which can translate into high costs. We present all underlying concepts of energy debt, bridging the connection with the existing concept of technical debt and show how to compute the energy debt through a motivational example. © 2020 ACM.

Abstract
Linear programming is a mathematical optimization technique used in numerous fields including mathematics, economics, and computer science, with numerous industrial contexts, including solving optimization problems such as planning routes, allocating resources, and creating schedules. As a result of its wide breadth of applications, a considerable amount of its user base is lacking in terms of programming knowledge and experience and thus often resorts to using graphical software such as Microsoft Excel. However, despite its popularity amongst less technical users, the methodologies used by these tools are often ad-hoc and prone to errors. To counteract this problem we propose creating a block-based language that allows users to create linear programming models using data contained inside spreadsheets. This language will guide the users to write syntactically and semantically correct programs and thus aid them in a way that current languages do not.

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The worldwide Web has dramatically evolved in recent years. Web pages are dynamic, expressed by programs written in common programming languages given rise to sophisticated Web applications. Thus, Web browsers are almost operating systems, having to interpret/compile such programs and execute them. Although JavaScript is widely used to express dynamic Web pages, it has several shortcomings and performance inefficiencies. To overcome such limitations, major IT powerhouses are developing a new portable and size/load efficient language: WebAssembly. In this paper, we conduct the first systematic study on the energy and run-time performance of WebAssembly and JavaScript on the Web. We used micro-benchmarks and also real applications in order to have more realistic results. Preliminary results show that WebAssembly, while still in its infancy, is starting to already outperform JavaScript, with much more room to grow. A statistical analysis indicates that WebAssembly produces significant performance differences compared to JavaScript. However, these differences differ between micro-benchmarks and real-world benchmarks. Our results also show that WebAssembly improved energy efficiency by 30%, on average, and showed how different WebAssembly behaviour is among three popular Web Browsers: Google Chrome, Microsoft Edge, and Mozilla Firefox. Our findings indicate that WebAssembly is faster than JavaScript and even more energy-efficient. Additionally, our benchmarking framework is also available to allow further research and replication.

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