This paper proposes an approach to managing domain analysis in Software Product Lines (SPLs) using Decision Tables (DTs) that are adapted to the unique characteristics of SPLs. The adapted DTs enable clear and explicit representation of the intricate decisions involved in deriving each software product. Additionally, a method is presented for detecting and resolving anomalies that may disrupt proper product derivation. The effectiveness of the approach is evaluated through a case study, which suggests that it has the potential to significantly reduce development time and costs for SPLs. Future research directions include investigating the integration of SAT solvers or other methods to improve specific cases of scalability and conducting empirical validation to further assess the effectiveness of the proposed approach.

Software process monitoring is a complex activity. Recently many authors have been suggesting the use of Statistical Process Control (SPC) for software process monitoring while others have pointed out potential pitfalls in using this approach. SPC is often used "as is" without the appropriate customizations or extensions to software, given the peculiarities and differences of software processes compared to manufacturing ones. As so the starting point to understand how and whether SPC can be used in software is to understand its contribution in monitoring processes. This work puts together experience collected by the authors in using SPC in industrial contexts, points out the main issues concerning software process monitoring and highlights how the technique addresses them. The main contribution of the paper is to formalize and put a set of guidelines together in a disciplined process for guiding practitioners in correctly using SPC during process monitoring.

Effort estimation is a long faced problem, but, in spite of the amount of research spent in this field, it still remains an open issue in the software engineering community. In two previous works the authors proposed an approach named dynamic calibration for effort estimation of software projects. In this paper they present a tool named SPEED that implements the dynamic calibration approach

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.