Jácome Cunha

The use of powerful mobile devices, like smartphones, tablets and laptops, are changing the way programmers develop software. 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. This paper presents a technique and a tool to detect anomalous energy consumption in Android applications, and to relate it directly with the source code of the application. We propose a dynamically calibrated model for energy consumption for the Android ecosystem, and that supports different devices. The model is then used as an API to monitor the application execution: first, we instrument the application source code so that we can relate energy consumption to the application source code; second, we use a statistical approach, based on fault-localization techniques, to localize abnormal energy consumption in the source code.

Energy consumption is becoming an evident concern to software developers. This is even more notorious due to the propagation of mobile devices. Such propagation of devices is also influencing software development: a software system is now developed has a set of similar products sharing common features.
In this short paper, we describe our methodology aim at static and accurately predict the energy consumption of software products in such variability systems, typically called software product lines.

Spreadsheets are considered to be the most widely used programming language in the world, and reports have shown that 90% of real-world spreadsheets contain errors. In this work, we try to identify spreadsheet smells, a concept adapted from software, which consists of a surface indication that usually corresponds to a deeper problem. Our smells have been integrated in a tool, and were computed for a large spreadsheet repository. Finally, the analysis of the results we obtained led to the refinement of our initial catalog.

Many errors in spreadsheet formulas can be avoided if spreadsheets are built automatically from higher-level models that can encode and enforce consistency constraints. However, designing such models is time consuming and requires expertise beyond the knowledge to work with spreadsheets. Legacy spreadsheets pose a particular challenge to the approach of controlling spreadsheet evolution through higher-level models, because the need for a model might be overshadowed by two problems: (A) The benefit of creating a spreadsheet is lacking since the legacy spreadsheet already exists, and (B) existing data must be transferred into the new model-generated spreadsheet. To address these problems and to support the model-driven spreadsheet engineering approach, we have developed a tool that can automatically infer ClassSheet models from spreadsheets. To this end, we have adapted a method to infer entity/relationship models from relational database to the spreadsheets/ClassSheets realm. We have implemented our techniques in the HAEXCEL framework and integrated it with the ViTSL/Gencel spreadsheet generator, which allows the automatic generation of refactored spreadsheets from the inferred ClassSheet model. The resulting spreadsheet guides further changes and provably safeguards the spreadsheet against a large class of formula errors. The developed tool is a significant contribution to spreadsheet (reverse) engineering, because it fills an important gap and allows a promising design method (ClassSheets) to be applied to a huge collection of legacy spreadsheets with minimal effort.

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.

Spreadsheets can be viewed as programming languages for non-professional programmers. These so-called ``end-user'' programmers vastly outnumber professional programmers creating millions of new spreadsheets every year. As a programming language, spreadsheets lack support for abstraction, testing, encapsulation, or structured programming. As a result, and as numerous studies have shown, the high rate of production is accompanied by an alarming high rate of errors. Some studies report that up to 90% of real-world spreadsheets contain errors. After their initial creation, many spreadsheets turn out to be used for storing and processing increasing amounts of data and supporting increasing numbers of users over long periods of time, making them complicated systems. An emerging solution to handle the complex and evolving software systems is Model-driven Engineering (MDE). To consider models as first class entities and any software artifact as a model or a model element is one of the basic principles of MDE. We adopted some techniques from MDE to solve spreadsheet problems. Most spreadsheets (if not all) lack a proper specification or a model. Using reverse engineering techniques we are able to derive various models from legacy spreadsheets. We use functional dependencies (a formalism that allow us to define how some column values depend on other column values) as building blocks for these models. Models can be used for several spreadsheet improvements, namely refactoring, safe evolution, migration or even generation of edit assistance. The techniques presented in this work are available under the framework HAEXCEL that we developed. It is composed of online and batch tools, reusable HASKELL libraries and OpenOffice.org extensions. A study with several end-users was organized to survey the impact of the techniques we designed. The results of this study indicate that the models can bring great benefits to spreadsheet engineering helping users to commit less errors and to work faster.

This paper presents a graphical interface to query model-driven spreadsheets, based on experience with previous work and empirical studies in querying systems, to simplify query construction for typical end-users with little to no knowledge of SQL. 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.

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In this paper we present a quality model for spreadsheets, based on the ISO/IEC 9126 standard that defines a generic quality model for software. To each of the software characteristics defined in the ISO/IEC 9126, we associate an equivalent spreadsheet characteristic. Then, we propose a set of spreadsheet specific metrics to assess the quality of a spreadsheet in each of the defined characteristics. In order to obtain the normal distribution of expected values for a spreadsheet in each of the metrics that we propose, we have executed them against all spreadsheets in the large and widely used EUSES spreadsheet corpus. Then, we quantify each characteristic of our quality model after computing the values of our metrics, and we define quality scores for the different ranges of values. Finally, to automate the atribution of a quality score to a given spreadsheet, according to our quality model, we have integrated the computation of the metrics it includes in both a batch and a web-based tool.

Although spreadsheets can be seen as a flexible programming environment, they lack some of the concepts of regular programming languages, such as structured data types. This can lead the user to edit the spreadsheet in a wrong way and perhaps cause corrupt or redundant data. We devised a method for extraction of a relational model from a spreadsheet and the subsequent embedding of the model back into the spreadsheet to create a model-based spreadsheet programming environment. The extraction algorithm is specific for spreadsheets since it considers particularities such as layout and column arrangement. The extracted model is used to generate formulas and visual elements that are then embedded in the spreadsheet helping the user to edit data in a correct way. We present preliminary experimental results from applying our approach to a sample of spreadsheets from the EUSES Spreadsheet Corpus.

Spreadsheets can be viewed as a highly flexible end-users programming environment which enjoys wide-spread adoption. But spreadsheets lack many of the structured programming concepts of regular programming paradigms. In particular, the lack of data structures in spreadsheets may lead spreadsheet users to cause redundancy, loss, or corruption of data during edit actions. In this paper, we demonstrate how implicit structural properties of spreadsheet data can be exploited to offer edit assistance to spreadsheet users. Our approach is based on the discovery of functional dependencies among data items which allow automatic reconstruction of a relational database schema. From this schema, new formulas and visual objects are embedded into the spreadsheet to offer features for auto-completion, guarded deletion, and controlled insertion. Schema discovery and spreadsheet enhancement are carried out automatically in the background and do not disturb normal user experience.

This paper proposes and validates a model-driven software engineering technique for spreadsheets. The technique that we envision builds on the embedding of spreadsheet models under a widely used spreadsheet system, so that models and their conforming instances are developed under the same environment. In practice, this convenient environment enhances evolution steps at the model level while the corresponding instance is automatically co-evolved. Finally, we have designed and conducted an empirical study with human users in order to assess our technique in production environments. The results of this study are promising and suggest that productivity gains are realizable under our model-driven spreadsheet development setting.

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.

This tool demo paper presents SmellSheet Detective: a tool for automatically detecting bad smells in spreadsheets. We have defined a catalog of bad smells in spreadsheet data which was fully implemented in a reusable library for the manipulation of spreadsheets. This library is the building block of the SmellSheet Detective tool, that has been used to detect smells in large, real-world spreadsheet within the EUSES corpus, in order to validate and evolve our bad smells catalog.

n this paper, we present MDSHEET, a framework for the embedding, evolution and inference of spreadsheet models. This framework offers a model-driven software development mechanism for spreadsheet users.

Spreadsheets are widely recognized as popular programming systems with a huge number of spreadsheets being created every day. Also, spreadsheets are often used in the decision processes of profit-oriented companies. While this illustrates their practical importance, studies have shown that up to 90% of real-world spreadsheets contain errors. In order to improve the productivity of spreadsheet end-users, the software engineering community has proposed to employ model-driven approaches to spreadsheet development. In this paper we describe the evaluation of a bidirectional model-driven spreadsheet environment. In this environment, models and data instances are kept in conformity, even after an update on any of these artifacts. We describe the issues of an empirical study we plan to conduct, based on our previous experience with end-user studies. Our goal is to assess if this model-driven spreadsheet development framework does in fact contribute to improve the productivity of spreadsheet users.

Software refactoring is a well-known technique that provides transformations on software artifacts with the aim of improving their overall quality.

In the past, we have proposed a catalog of refactoring 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 (the spreadsheet data) automatically co-evolves after a refactoring is applied at the model level. Our motivation for such research 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 presented not only in terms of productivity changes between refactored and non-refactored scenarios, but also in terms of overall user satisfaction, relevance, and experience.

In almost all cases the refactorings indeed improved end-users productivity. Moreover, in most cases users were more engaged with the refactored version of the spreadsheets they worked with.