Jorge Mendes

MDSheet: a framework for model-driven spreadsheet engineering

In 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.

Bidirectional transformation of model-driven spreadsheets

Spreadsheets play an important role in software organizations. Indeed, in large software organizations, spreadsheets are not only used to define sheets containing data and formulas, but also to collect information from different systems, to adapt data coming from one system to the format required by another, to perform operations to enrich or simplify data, etc. In fact, over time many spreadsheets turn out to be used for storing and processing increasing amounts of data and supporting increasing numbers of users. Unfortunately, spreadsheet systems provide poor support for modularity, abstraction, and transformation, thus, making the maintenance, update and evolution of spreadsheets a very complex and error-prone task. We present techniques for model-driven spreadsheet engineering where we employ bidirectional transformations to maintain spreadsheet models and instances synchronized. In our setting, the business logic of spreadsheets is defined by ClassSheet models to which the spreadsheet data conforms, and spreadsheet users may evolve both the model and the data instances. Our techniques are implemented as part of the MDSheet framework: an extension for a traditional spreadsheet system.

Embedding and evolution of spreadsheet models in spreadsheet systems

This paper describes the embedding of ClassSheet models in spreadsheet systems. ClassSheet models are well-known and describe the business logic of spreadsheet data. We embed this domain specific model representation on the (general purpose) spreadsheet system. By defining such an embedding, we provide end users a model-driven engineering spreadsheet developing environment. End users can interact with both the model and the spreadsheet data in the same environment. Moreover, we use advanced techniques to evolve spreadsheets and models and to have them synchronized. In this paper we present our work on extending a widely used spreadsheet system with such a model-driven spreadsheet engineering environment.

SmellSheet detective: A tool for detecting bad smells in spreadsheets

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 spreadsheets within the EUSES corpus, in order to validate and evolve our bad smells catalog.

Querying model-driven spreadsheets

Spreadsheets are being used with many different purposes that range from toy applications to complete information systems. In any of these cases, they are often used as data repositories that can grow significantly. As the amount of data grows, it also becomes more difficult to extract concrete information out of them. This paper focuses on the problem of spreadsheet querying. In particular, we propose an expressive and composable technique where intuitive queries can be defined. Our approach builds on a model-driven spreadsheet development environment, and queries are expressed referencing entities in the model of a spreadsheet instead of in its actual data. Finally, the system that we have implemented relies on Googles query function for spreadsheets.

Model-based programming environments for spreadsheets

Spreadsheets can be seen as a flexible programming environment. However, 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.Finally, we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze here the results achieved. We infer a relational model from spreadsheet data.The extracted model is embedded into the spreadsheet.We develop a model-based spreadsheet programming environment.The environment provides advanced editing assistance.We validate our techniques via an empirical study.

Extension and implementation of ClassSheet models

In this paper we explore the use of models in the context of spreadsheet engineering. We review a successful spreadsheet modeling language, whose semantics we further extend. With this extension we bring spreadsheet models closer to the business models of spreadsheets themselves. An addon for a widely used spreadsheet system, providing bidirectional model-driven spreadsheet development, was also improved to include the proposed model extension.

Embedding, Evolution, and Validation of Model-Driven Spreadsheets

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. This means that we enable the creation and evolution of spreadsheet models under a spreadsheet system. More precisely, we embed ClassSheets, a visual language with a syntax similar to the one offered by common spreadsheets, that was created with the aim of specifying spreadsheets. Our embedding allows models and their conforming instances to be 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.

QuerySheet: A bidirectional query environment for model-driven spreadsheets

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.

Towards an evaluation of bidirectional model-driven spreadsheets

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.