Using constraints to diagnose faulty spreadsheets
Author(s) -
Rui Abreu,
Birgit Hofer,
Alexandre Perez,
Franz Wotawa
Publication year - 2014
Publication title -
software quality journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 43
eISSN - 1573-1367
pISSN - 0963-9314
DOI - 10.1007/s11219-014-9236-4
Subject(s) - debugging , computer science , set (abstract data type) , constraint (computer aided design) , constraint satisfaction problem , programming language , process (computing) , software engineering , test case , constraint programming , artificial intelligence , machine learning , engineering , mathematical optimization , mechanical engineering , regression analysis , mathematics , probabilistic logic , stochastic programming
Spreadsheets can be viewed as a highly flexible programming environment for end users. Spreadsheets are widely adopted for decision making and may have a serious economical impact for the business. However, spreadsheets are staggeringly prone to errors. Hence, approaches for aiding the process of pinpointing the faulty cells in a spreadsheet are of great value. We present a constraint-based approach, ConBug, for debugging spreadsheets. The approach takes as input a (faulty) spreadsheet and a test case that reveals the fault and computes a set of diagnosis candidates for the debugging problem. Therefore, we convert the spreadsheet and a test case to a constraint satisfaction problem. We perform an empirical evaluation with 78 spreadsheets from different sources, where we demonstrate that our approach is light-weight and efficient. From our experimental results, we conclude that ConBug helps end users to pinpoint faulty cells.
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