Abstract
Only recently has the software engineering community started conducting research on developing energy efficient software, or green software. This is shadowed when compared to the research already produced in the computer hardware community. While research in green software is rapidly increasing, several recent studies with software engineers show that they still miss techniques, knowledge, and tools to develop greener software. Indeed, all such studies suggest that green software should be part of a modern Computer Science Curriculum. In this paper, we present survey results from both researchers' and educators' perspective on green software education. These surveys confirm the lack of courses and educational material for teaching green software in current higher education. Additionally, we highlight three key pedagogical challenges in bringing green software to computer science curriculum and discussed existing solutions to address these key challenges. We firmly believe that 'green thinking"and the broad adoption of green software in computer science curriculum can greatly benefit our environment, society, and students in an era where software is everywhere and evolves in an unprecedented speed. © 2021 Owner/Author.

Abstract
Microservices emerged as one of the most popular architectural patterns in the recent years given the increased need to scale, grow and flexibilize software projects accompanied by the growth in cloud computing and DevOps. Many software applications are being submitted to a process of migration from its monolithic architecture to a more modular, scalable and flexible architecture of microservices. This process is slow and, depending on the project's complexity, it may take months or even years to complete. This paper proposes a new approach on microservice identification by resorting to topic modelling in order to identify services according to domain terms. This approach in combination with clustering techniques produces a set of services based on the original software. The proposed methodology is implemented as an open-source tool for exploration of monolithic architectures and identification of microservices. A quantitative analysis using the state of the art metrics on independence of functionality and modularity of services was conducted on 200 open-source projects collected from GitHub. Cohesion at message and domain level metrics' showed medians of roughly 0.6. Interfaces per service exhibited a median of 1.5 with a compact interquartile range. Structural and conceptual modularity revealed medians of 0.2 and 0.4 respectively. Our first results are positive demonstrating beneficial identification of services due to overall metrics' results. © 2021 ACM.

Abstract
Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software development approach, where the core concept is to study the systematic development of products that can be deployed in a variable way, e.g., to include different features for different clients. The traditional approach for measuring energy consumption in SPLs is to generate and individually measure all products, which, given their large number, is impractical. We present a technique, implemented in a tool, to statically estimate the worst-case energy consumption for SPLs. The goal is to reason about energy consumption in all products of a SPL, without having to individually analyze each product. Our technique combines static analysis and worst-case prediction with energy consumption analysis, in order to analyze products in a feature-sensitive manner: a feature that is used in several products is analyzed only once, while the energy consumption is estimated once per product. This paper describes not only our previous work on worst-case prediction, for comprehensibility, but also a significant extension of such work. This extension has been realized in two different axis: firstly, we incorporated in our methodology a simulated annealing algorithm to improve our worst-case energy consumption estimation. Secondly, we evaluated our new approach in four real-world SPLs, containing a total of 99 software products. Our new results show that our technique is able to estimate the worst-case energy consumption with a mean error percentage of 17.3% and standard deviation of 11.2%.

Abstract
In the early days of the world wide web, browsers were developed to navigate through (static) HTML web page documents. This has changed dramatically, and nowadays web pages are dynamic, expressed by programs written in regular programming languages. As a result, browsers are almost operating systems, having to interpret/compile such programs and execute them within the browser itself. Currently, while JavaScript is the main de facto language to express web pages, it does have various short comings and performance inefficiencies. WebAssembly, a new portable and size/load efficient alternative developed by major IT powerhouses, is seen as the future substitute. As WebAssembly aims to be more performance efficient than JavaScript, we aim to look at this current status and present a preliminary study on the performance of these two, based on their runtime and energy efficiency. Preliminary results show that WebAssembly, while still in its infancy, is starting to already challenge JavaScript, with much more room to grow. Additionally, our benchmarking framework is also made available to allow further research and replication.

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
Functional programmers are strong enthusiasts of modular solutions to programming problems. Since software characteristics such as readability or maintainability are often directly proportional to modularity, this programming style naturally contributes to the beauty of functional programs. Unfortunately, in return of this beauty we often sacrifice efficiency: modular programs rely, at runtime, on the creation, use and elimination of intermediate data structures to connect its components. In this tutorial paper, we study an advanced technique that attempts to retain the best of this two worlds: (i) it allows programmers to implement beautiful, modular programs (ii) it shows how to transform such programs, in a way that can be incorporated in a compiler, into programs that do not construct any intermediate structure. © 2019, Springer Nature Switzerland AG.