Abstract
This paper presents a tool, named QUERYSHEET, to query spreadsheets. We defined a language to write the queries, which resembles SQL, the language to query databases. This allows to write queries which are more related to the spreadsheet content than with current approaches.

Abstract

Abstract
This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings. © 2016 ACM.

Abstract
While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. Additionally, a growing number of developers wish to become more energy-aware when programming and feel a lack of tools and the knowledge to do so. In this paper we define a ranking of energy efficiency in programming languages. We consider a set of computing problems implemented in ten well-known programming languages, and monitored the energy consumed when executing each language. Our preliminary results show that although the fastest languages tend to be the lowest consuming ones, there are other interesting cases where slower languages are more energy efficient than faster ones.

Abstract
This paper briefly proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrum-based fault localization, is introduced to relate energy consumption to the system's source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code.

Abstract
This paper briefly presents a new approach for helping developers identify energy hot spots in their applications. Using tests cases, and statistical methods based on Spectrum-based Fault Localization, high energy consumption is related to the system's source code and a ranking of possible energy leaks are pointed. This technique is both language independent, and context level independent. Initial studies have also shown that using this technique helped developers identify and optimize energy problems in half the time while improving the energy efficiency by 18%. © 2017 IEEE.