The Fallacy of Shotgun Correlations for Software Measures

Richard E. Courtney and David A. Gustafson
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Richard E. Courtney: Tulane University, Computer Science
David A. Gustafson: Kansas State University, Computing and Information Sciences

A chapter in Computing Science and Statistics, 1992, pp 318-322 from Springer

Abstract: Abstract Many software measures have been forwarded on the simple basis of a high linear correlation coefficient with some measurable quantities. The linear correlation coefficient is an unreliable statistic for deciding whether an observed correlation indicates significant association. Several published software measure experiments collected upwards of 20 different measurements or have fourteen or fewer observations. With considerable data from small samples, the probability of “discovering” a “significant” correlation is high. We present a computer simulation experiment where the correlation between sets of randomly generated numbers is calculated. We also look at randomly generated numbers in the ranges that would be expected in Halstead’s Software Science measures. Our results show that the average maximum linear correlation for randomly generated numbers is.70 or higher if the sample size is low compared to the number of variables. Alternative statistical approaches to obtain meaningful significant results is presented.

Keywords: Average Maximum; Software Project; Maximum Correlation; Software Measure; Cyclomatic Complexity (search for similar items in EconPapers)
Date: 1992
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DOI: 10.1007/978-1-4612-2856-1_45

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