Improving measurement certainty by using calibration to find systematic measurement error—a case of lines-of-code measure

Abstract

Base measures such as the number of lines-of-code are often used to make predictions about such phenomena as project effort, product quality or maintenance effort. However, quite often we rely on the measurement instruments where the exact algorithm for calculating the value of the measure is not known. The objective of our research is to explore how we can increase the certainty of base measures in software engineering. We conduct a benchmarking study where we use four measurement instruments for lines-of-code measurement with unknown certainty to measure five code bases. Our results show that we can adjust the measurement values by as much as 20% knowing the systematic error of the tool. We conclude that calibrating the measurement instruments can significantly contribute to increased accuracy in measurement processes in software engineering. This will impact the accuracy of predictions (e.g. of effort in software projects) and therefore increase the costefficiency of software engineering processes.