Abstract
This documents introduces \Energyware" as a software engineering discipline aiming at defining, analyzing and optimizing the energy consumption by software systems. In this paper we present energyware analysis in the context of programming languages, software data structures and program's source code. For each of these areas we describe the research work done in the context of the Green Software Laboratory at Minho University: we describe energyaware techniques, tools, libraries, and repositories. © 2018 by the paper's authors.

Abstract
Software developers are more and more eager to understand their code's energy performance. However, even with such knowledge it is difficult to know how to improve the code. Indeed, little tool support exists to understand the energy consumption profile of a software system and to eventually (automatically) improve its code. In this paper we present a tool termed jStanley which automatically finds collections in Java programs that can be replaced by others with a positive impact on the energy consumption as well as on the execution time. In seconds, developers obtain information about energy-eager collection usage. jStanley will further suggest alternative collections to improve the code, making it use less time, energy, or a combination of both. The preliminary evaluation we ran using jStanley shows energy gains between 2% and 17%, and a reduction in execution time between 2% and 13%. A video can be seen at https://greensoftwarelab.github.io/jStanley. © 2018 Association for Computing Machinery.

Abstract

Abstract
Energy consumption is an issue that involves all of us, both as individuals and as members of a society, and covers all our areas of activity. It is something so broad that its impact has important reflections on our social, cultural and financial structures. The domain of software, and in particular database systems, is not an exception. Although it seems to be a little bit strange to study the energy consumption of just one query, when we consider the execution of a a few thousand queries per second, quickly we see the importance of the querying consumption in the monthly account of any company that has a conventional data center. To demonstrate the energy consumption of queries in data centers, we idealized a small dashboard for monitoring and analyzing the sales of a company, and implemented all the queries needed for populating it and ensuring its operation. The queries were organized into two groups, oriented especially to two distinct database management systems: one relational (MySQL) and one non relational (Neo4J). The goal is to evaluate the energy consumption of different types of queries, and at the same time compare it in terms of relational and non-relational database approaches. This paper relates the process we implemented to set up the energy consumption application scenario, measure the energy consumption of each query, and present our first preliminary results.

Abstract
Shortcut fusion is a well-known optimization technique for functional programs. Its aim is to transform multi-pass algorithms into single pass ones, achieving deforestation of the intermediate structures that multi-pass algorithms need to construct. Shortcut fusion has already been extended in several ways. It can be applied to monadic programs, maintaining the global effects, and also to obtain circular and higher-order programs. The techniques proposed so far, however, only consider programs defined as the composition of a single producer with a single consumer. In this paper, we analyse shortcut fusion laws to deal with programs consisting of an arbitrary number of function compositions.

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