I often tell my students that the absolute values of measures do not always say much. Take an example of McCabe cyclomatic complexity – the number of 100 (meaning 100 independent paths through a method) does not need to denote problems. It could be a large switch statement which changes locale based on the IP address type. However, it is important to monitor thresholds of measures, based on the meaning of the function and the problem at hand.
In this article from IST, “Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison” (https://doi.org/10.1016/j.infsof.2017.11.005), we can learn three different types of finding thresholds for software measures – ROC curves, VARL, and Alves ranking (named after the author of the method). This article shows how well we can predict the fault-proneness of modules if we use thresholds rather than absolute value.
Have a nice reading!
