CheckCell promises to finger all-too-common Excel spreadsheet errors

UMass researchers apply "threshold of unusualness" to spot Excel problems

University of Massachusetts Amherst researchers have released a tool called CheckCell that's designed to spot errors in Microsoft Excel spreadsheets that could lead to big problems.

And as researchers will tell you, most spreadsheets do tend to have errors in them.

CheckCell, available as a free Excel software plug-in on GitHub, was the topic of a presentation delivered last week by UMass computer science doctoral student Daniel Barowy at  the Object-Oriented Programming, Systems, Languages and Applications (OOPSLA) conference in Portland, Ore. (Here's the CheckCell research paper.)

UMass Amherst Dan Barowy UMass Amherst

Doctoral student Dan Barowy showed off CheckCell

Barowy joined fellow grad student Dimitar Gochev and advisor Emery Berger, a UMass School of Computer Science professor, to develop CheckCell. The researchers say spreadsheet errors can have serious consequences, whether it's messing up a student's grades or leading to erroneous research data becoming accepted as fact.

Microsoft apparently is all for researchers bolstering its spreadsheet: Microsoft Research funded the project.

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Berger says in a statement: "Our work for the first time combines data analysis and program analysis. Poor quality data costs everyone money. CheckCell helps users avoid costly mistakes."

He adds, "Basically, CheckCell identifies data points that have a big impact on the final result, even if the impact is super subtle and difficult to detect. CheckCell immediately flags data points that are very suspicious, the ones that deserve a second look. It's like having a helper who says, 'pay attention to these cells, they really matter.'"

The researchers' technique for pinpointing Excel errors uses what Berger calls "a threshold of unusualness" in which questionable data points are marked for spreadsheet designers to double check. This data debugging approach addresses shortcomings in simple testing and static analysis efforts designed to root out bugs in programs.

Next up is applying the technology to Big Data.

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