Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/33782
Título: | Model inference for spreadsheets |
Autor(es): | Mendes, Jorge Erwig, Martin Cunha, Jácome Miguel Costa Saraiva, João Alexandre |
Palavras-chave: | Spreadsheets ClassSheets Relational model Automatic model inference Empirical validation |
Data: | 2016 |
Editora: | Springer |
Revista: | Automated Software Engineering |
Resumo(s): | Many errors in spreadsheet formulas can be avoided if spreadsheets are built automati- cally from higher-level models that can encode and enforce consistency constraints in the generated spreadsheets. Employing this strategy for legacy spreadsheets is dificult, because the model has to be reverse engineered from an existing spreadsheet and existing data must be transferred into the new model-generated spreadsheet. We have developed and implemented a technique that automatically infers relational schemas from spreadsheets. This technique uses particularities from the spreadsheet realm to create better schemas. We have evaluated this technique in two ways: First, we have demonstrated its appli- cability by using it on a set of real-world spreadsheets. Second, we have run an empirical study with users. The study has shown that the results produced by our technique are comparable to the ones developed by experts starting from the same (legacy) spreadsheet data. Although relational schemas are very useful to model data, they do not t well spreadsheets as they do not allow to express layout. Thus, we have also introduced a mapping between relational schemas and ClassSheets. A ClassSheet controls further changes to the spreadsheet and safeguards it against a large class of formula errors. The developed tool is a contribution to spreadsheet (reverse) engineering, because it lls an important gap and allows a promising design method (ClassSheets) to be applied to a huge collection of legacy spreadsheets with minimal effort. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/33782 |
DOI: | 10.1007/s10515-014-0167-x |
ISSN: | 0928-8910 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | HASLab - Artigos em revistas internacionais |