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Data cleaning: The next step in empirical software engineering

Over the last 10 years software engineering researchers have gone from a state of data famine to being deluged with data. Until recently these researchers have been acting like children at a birthday party, rushing around unwrapping all the presents to see what is inside and quickly moving onto the next one. A good example of this are those papers purporting to have found a power law relationship between two constructs by simply plotting the data using log axis and drawing a straight line through the data; hey look, a power law, isn’t that interesting? Hopefully, these days, reviewers are starting to wise up and insist that any claims of a power law be checked.

Data cleaning is a very important topic that unfortunately appears to be missing from many researchers’ approach to data analysis. The quality of a model built from data is only as good as the quality of the data used to build it. Anybody who is interested in building models that connect to the real world of software engineering, rather than just getting another paper published, has to consider the messiness that gets added to data by the software developers who are intimately involved in the processes that generated the artifacts (e.g., source code, bug reports).

I have jut been reading a paper containing some unsettling numbers (It’s not a Bug, it’s a Feature: On the Data Quality of Bug Databases). A manual classification of over 7,000 issues reported against various large Java applications found that 42.6% of the issues were misclassified (e.g., a fault report was actually a request for enhancement), resulting in a change of status of 39% of the files once thought to contain a fault to not actually containing a fault (any fault prediction models built assuming the data in the fault database was correct now belong in the waste bin).

What really caught my eye about this research was the 725 hours (90 working days) invested by the researchers doing the manual classification (one person + independent checking by another). Anybody can extracts counts of this that and the other from the many repositories now freely available, generate fancy looking plots from them and add in some technobabble to create a paper. Real researchers invest lots of their time figuring out what is really going on.

These numbers are a wakeup call for all software engineering researchers. The data you are using needs to be thoroughly checked and be prepared to invest a lot of time doing it.

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