In silico studies allow the use of computer models to simulate the environment, object, and subject behavior of an experiment. They are valuable tools to support the quantitative analysis of the behavior of different types of projects because they allow the use of different techniques such as stochastic and analytical methods to evaluate the impact of project decisions without the real execution costs. Recently, in silico studies have been used to study distributed software development projects. In this paper, we present a simulation model defined by a set of variables that describe the dynamics of Distributed Software Development (DSD) projects. The assessment of our simulation model is based on a Systematic Literature Review (SLR) where we seek evidence, from primary studies published until October 2018, that the variables and relationships in our model do exist in actual DSD projects. We use a SLR as a validation mechanism. Our results are twofold. First, there is a gap of studies on some variables including knowledge expertise and task allocation strategy in distributed software development projects. And, second, there still is a lack of data to support results related to productivity, especially testing activities, on DSD projects. Despite the problem of lack of common terminology for reporting the results on DSD studies, we argue that we need more studies to be able to clearly infer the dynamics of the relationship between variables on distributed software development. Our study brings one step closer to fulfill this gap.

Risk Assessment is a growing discipline on the context of distributed software projects. The complexity to develop software and the related knowledge require automated support for project managers in order to analyze actions to reduce the project's risks and measure the impact of such actions. My thesis aims to investigate an approach for the assessment of risks in globally distributed software projects. This research proposes to apply stochastic simulation technique to analyze project data and identify factors that are likely to impact team productivity and that could affect the team's ability to meet its schedule objective. We aim this thesis to be applied by project management groups to perform risk assessment early in the software development process and to help decision-making process.

We describe an empirical approach for the assessment of the scheduling risk in a large globally distributed industrial software project. We analyzed two and half years of project data and we identified some factors that are likely to impact team productivity and that could affect the teampsilas ability to meet its schedule objective. We built an analytical model to capture teampsilas productivity variability, which can be used to approximate the total time required to complete the project. Our approach is designed to be used by project management groups to perform risk assessment early in the software development process.