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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.

An essential part of a software engineering education is technology innovation. Indeed software engineers, as future practitioners, must be able to identify the most appropriate technologies to adopt in projects. As so, it is important to develop the skills that will allow them to evaluate and make decisions on tools, technologies, techniques and methods according to the available empirical evidence reported in literature. In this sense, a rigorous manner for analyzing and critically addressing literature is Systematic Review. It requires formalizing an answerable research question according to the problem or issues to face; search the literature for available evidence according to a systematic protocol and retrieve data from the identified sources; analyze the collected evidence and use it to support decision making and conclusions. In this paper we report on how Systematic Review has been integrated in the "Empirical Software Engineering Methods" course that is taught at the Department of Informatics at the University of Bari, and how students have been introduced to this type of literature review through a hands-on approach. As far as we know, it is the first attempt of including a complex topic like systematic review in a university course on empirical software engineering. We have no empirical evidence on the effectiveness of the approach adopted, other than practice-based experience that we have acquired. Nonetheless, we have collected qualitative data through a questionnaire submitted to the students of the course. Their positive answers and impressions are a first informal confirmation of the successful application of our strategy

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Measurement based software process improvement needs a non-intrusive approach to determine what and where improvement is needed without knowing anything about the methods and techniques used during project execution. Beside, it is necessary for obtaining successful business management, an accurate process behavior prediction. In order to obtain these results we proposed to use Statistical Process Control (SPC) tailored to the software process point of view. The paper proposes an appropriate SPC-Framework and presents two industrial experiences in order to validate the framework in two different software contexts: recalibration of effort estimation models; monitoring of the primary processes through the supporting ones. These experiences validate the framework and show how it can be successfully used as a decision support tool in software process improvement.