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Identify Outliers, Understand the Process
journal contribution
posted on 2009-01-01, 00:00 authored by Mark C. Paulk, Kim Lascola Needy, Jayant RajgopalIdentifying atypical performance in a software
process or atypical entities in software
data is important for statistically analyzing
processes and products and for statistical
process control (SPC) of the software process.
In this research, data from the Personal
Software Process (PSP) was analyzed using
two different techniques for identifying
atypical observations. It was found that
simple techniques such as interquartile limits
are approximately as effective as XmR
control charts for identifying outliers in the
absence of causal analysis. When outliers are
appropriately removed from a process, the
remaining data characterizes the “commoncause”
system of typical implementation.
The review rate in the PSP common-cause
system was found to be faster than the
recommended 100 to 200 lines of code per
hour. Following recommended practice is a
precursor to SPC.