Improving Measurement Certainty by Using Calibration to Find Systematic Measurement Error - A Case of Lines-of-Code Measure

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