The lines of code metric as a predictor of program faults: a critical analysis

The relationship between measures of software complexity and programming errors is explored. Four distinct regression models were developed for an experimental set of data to create a predictive model from software complexity metrics to program errors. The lines of code metric, traditionally associated with programming errors in predictive models, was found to be less valuable as a criterion measure in these models than measures of software control complexity. A factor analytic technique used to construct a linear compound of lines of code with control metrics was found to yield models of superior predictive quality.<>