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Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health
For example, for project health indicators such as $C$= number of commits; $I$=number of closed issues, and $R$=number of closed pull requests, niSNEAK's 12 month prediction errors are \{I=0\%, R=33\%\, C=47\%\} Based on the above, we recommend landscape analytics (e. g. niSNEAK) especially when learning from very small data sets.
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