Improving dynamic calibration through statistical process control

Dynamic calibration (DC), presented by the authors in previous works has proved to be a flexible approach for massive maintenance software project estimation, able to recalibrate an estimation model in use according to relevant process performance changes pointed out by the project manager. Nevertheless, it results quite subjective in its application and tightly based on manager experience. In this work the authors present an improvement of the approach based on the use of statistical process control (SPC) technique. SPC is a statistically based method able to quickly highlight shift in process performances. It is well known in manufacturing contexts and it has recently emerged in the software engineering community. In this work, authors have integrated SPC in DC as decision support tool for identifying when recalibration of the estimation model must be carried out. This extension makes DC less "person-based", more deterministic and transferable in its use than the previous version. The extended approach has been experimented on industrial data related to a renewal project and the results compared with both, a concurrent approach such as analogy based estimation and its previous version. The results are encouraging and stimulate further investigation.