Improving Measurement Certainty by Using Calibration to Find Systematic Measurement Error - A Case of Lines-of-Code Measure
Paper in proceeding, 2016
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
cost-effciency of software engineering processes.