Abstract
Software refactoring is a well-known technique that provides transformations on software artifacts with the aim of improving their overall quality. We have previously proposed a catalog of refactorings for spreadsheet models expressed in the ClassSheets modeling language, which allows us to specify the business logic of a spreadsheet in an object-oriented fashion. Reasoning about spreadsheets at the model level enhances a model-driven spreadsheet environment where a ClassSheet model and its conforming instance (spreadsheet data) automatically co-evolves after applying a refactoring at the model level. Research motivation was to improve the model and its conforming instance: the spreadsheet data. In this paper we define such refactorings using previously proposed evolution steps for models and instances. We also present an empirical study we designed and conducted in order to confirm our original intuition that these refactorings have a positive impact on end-user productivity, both in terms of effectiveness and efficiency. The results are not only presented in terms of productivity changes between refactored and nonrefactored scenarios, but also the overall user satisfaction, relevance, and experience. In almost all cases the refactorings improved end-users productivity. Moreover, in most cases users were more engaged with the refactored version of the spreadsheets they worked with. 2016 Elsevier Inc. All rights reserved.

Abstract
This paper presents GreenDroid, a tool for monitoring and analyzing power consumption for the Android ecosystem. This tool instruments the source code of a giving Android application and is able to estimate the power consumed when running it. Moreover, it uses advanced classification algorithms to detect abnormal power consumption and to relate them to fragments in the source code. A set of graphical results are produced that help software developers to identify abnormal power consumption in their source code.

Abstract
The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly. In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them. Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product. We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products.

Abstract
Spreadsheets are widely used not only to define mathematical expressions, but also to store large and complex data. To query such data is usually a difficult task to perform, usually for end user. In this work we embed the textual query language in the model-driven spreadsheet environment as a spreadsheet itself. The result is an expressive and powerful query environment that has knowledge of the business logic defined by the spreadsheet data (the spreadsheet model) to guide end users constructing correct queries.

Abstract
This paper presents ES-SQL, an embedded tool for visually constructing queries over spreadsheets. This tool provides an expressive query environment which has knowledge on the business logic of spreadsheets, and by this knowledge it assists the user in defining the intended queries.

Abstract
This paper presents a graphical interface to query model-driven spreadsheets to simplify query construction for typical end-users with little to no knowledge of SQL. This was based on experience with previous work and empirical studies in querying systems. We briefly show our previous text based model-driven querying system. Afterwards, we detail our graphical model-driven querying interface, explaining each part of the interface and showing an example. To validate our work, we executed an empirical study, comparing our graphical querying approach to an alternative querying tool, which produced positive results.