In the last decade, there has been increasing interest in pair programming (PP). However, despite the existing work, there is still a lack of substantial evidence of the effects of PP in industrial environments. To address this issue, we have analyzed the work of a team of 17 industrial developers for 14 months. The team is part of the IT department of a large Italian manufacturing company; it adopts a customized version of extreme programming (XP). We have investigated the effects of PP on software quality in five different scenarios. The results show that PP appears to provide a perceivable but small effect on the reduction of defects in these settings.

Pair Programming is one of the most studied and debated development techniques. However, at present, we do not have a clear, objective, and quantitative understanding of the claimed benefits of such development approach. All the available studies focus on the analysis of the effects of Pair Programming (e.g., code quality, development speed, etc.) with different findings and limited replicability of the experiments. This paper adopts a different approach that could be replicated in an easier way: it investigates how Pair Programming affects the way developers write code and interact with their development machine. In particular, the paper focuses on the effects that Pair Programming has on developers' attention and productivity. The study was performed on a professional development team observed for ten months and it finds out that Pair Programming helps developers to eliminate distracting activities and to focus on productive activities.

In this paper, we propose a method for predicting development effort based on user stories. Such approach is well suited for Agile software projects where requirements are developed along with the project and only sketched in a rough manner. We apply the proposed method to two industrial Agile software projects of very different size and structure. We show that such effort estimation works reasonably well if user stories are written in a structured way.

Developers use tools to develop software systems and always alleged better tools are being produced and purchased. Still there have been only limited studies on how people really use tools; these studies have used limited data, and the interactions between tools have not been properly elaborated. The advent of the AISEMA (Automated In-Process Software Engineering Measurement and Analysis) systems [3] has enabled a more detailed collection of tools data. Our "new idea" is to take advantage of such data to build a simple model based on an oriented graph that enables a good understanding on how tools are used individually and collectively. We have empirically validated the model analyzing an industrial team of 19 developers for a period of 10 months.

Novices' activities can be very different from those of experienced developers. The analysis of tool usage can propose early insights on novices integration in the team and also provide a method to compare their activities with experts. In this work we compare novices and experts in terms of tool usage, both in solo and in Pair Programming (PP). At the end of the integration process, novices activities are aligned to experts ones. Data for this study have been collected in an industrial team of 17 developers for a period of 10 months. The usage of AISEMA (Automated In-Process Software Engineering Measurement and Analysis) systems allowed us to have continuous, accurate and low cost measurement for our analysis.