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.

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.

Abstract
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.

Abstract
Sustainable development has become an increasingly important theme not only in the world politics, but also an increasingly central theme for the engineering professions around the world. Software engineers are no exception as shown in various recent research studies. Despite the intensive research on green software, today’s undergraduate computing education often fails to address our environmental responsibility. In this paper, we present a module on energy efficient software that we introduced as part of an advanced course on software analysis and testing. In this module students study techniques and tools to analyze and optimize energy consumption of software systems. Preliminary results of the first four instances of this course show that students are able to optimize the energy consumption of software systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
This tutorial aims to provide knowledge on a different facet of efficiency in data structures: energy efficiency. As many recent studies have shown, the main roadblock in regards to energy efficient software development are the misconceptions and heavy lack of support and knowledge, for energy-aware development, that programmers have. Thus, this tutorial aims at helping provide programmers more knowledge pertaining to the energy efficiency of data structures. We conducted two in-depth studies to analyze the performance and energy efficiency of various data structures from popular programming languages: Haskell and Java. The results show that within the Haskell programming language, the correlation between performance and energy consumption is statistically almost identical, while there are cases with more variation within the Java language. We have presented which data structures are more efficient for common operations, such as inserting and removing elements or iterating over the data structure. The results from our studies can help support developers in better understanding such differences within data structures, allowing them to carefully choose the most adequate implementation based on their requirements and goals. We believe that such results will help further close the gap when discussing the lack of knowledge in energy efficient software development. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.