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FaultySheet Detective: When Smells Meet Fault Localization, Abreu, Rui, Cunha Jácome, Fernandes João Paulo, Martins Pedro, Perez Alexandre, and Saraiva João , Proceedings of the 30th IEEE International Conference on Software Maintenance and Evolution, Washington, DC, USA, p.625–628, (2014) Abstracticsme14-td.pdf

This paper presents a tool, dubbed FaultySheet Detective, for aiding in spreadsheet fault localization, which combines the detection of bad smells with a generic spectrum-based fault localization algorithm.

From Relational ClassSheets to UML+OCL, Cunha, Jácome, Fernandes João Paulo, and Saraiva João , Proceedings of the Software Engineering Track at the 27th Annual ACM Symposium On Applied Computing (SAC 2012), p.1151–1158, (2012) Abstractsac-se12.pdf

Spreadsheets are among the most popular programming languages in the world. Unfortunately, spreadsheet systems were not tailored from scratch with modern programming language features that guarantee, as much as possible, program correctness. As a consequence, spreadsheets are populated with unacceptable amounts of errors. In other programming language settings, model-based approaches have been proposed to increase productivity and program effectiveness. Within spreadsheets, this approach has also been followed, namely by ClassSheets. In this paper, we propose an extension to ClassSheets to allow the specification of spreadsheets that can be viewed as relational databases. Moreover, we present a transformation from ClassSheet models to UML class diagrams enriched with OCL constraints. This brings to the spreadsheet realm the entire paraphernalia of model validation techniques that are available for UML.

From Spreadsheets to Relational Databases and Back, Cunha, Jácome, Saraiva João, and Visser Joost , Proceedings of the 2009 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation, New York, NY, USA, p.179–188, (2009) Abstractpepm09.pdf

This paper presents techniques and tools to transform spreadsheets into relational databases and back. A set of data refinement rules is introduced to map a tabular datatype into a relational database schema. Having expressed the transformation of the two data models as data refinements, we obtain for free the functions that migrate the data. We use well-known relational database techniques to optimize and query the data. Because data refinements define bidirectional transformations we can map such database back to an optimized spreadsheet. We have implemented the data refinement rules and we have constructed tools to manipulate, optimize and refactor Excel-like spreadsheets.