StakeNet: using social networks to analyse the stakeholders of large-scale software projects

Many software projects fail because they overlook stakeholders or involve the wrong representatives of significant groups. Unfortunately, existing methods in stakeholder analysis are likely to omit stakeholders, and consider all stakeholders as equally influential. To identify and prioritise stakeholders, we have developed StakeNet, which consists of three main steps: identify stakeholders and ask them to recommend other stakeholders and stakeholder roles, build a social network whose nodes are stakeholders and links are recommendations, and prioritise stakeholders using a variety of social network measures. To evaluate StakeNet, we conducted one of the first empirical studies of requirements stakeholders on a software project for a 30,000-user system. Using the data collected from surveying and interviewing 68 stakeholders, we show that StakeNet identifies stakeholders and their roles with high recall, and accurately prioritises them. StakeNet uncovers a critical stakeholder role overlooked in the project, whose omission significantly impacted project success

Local Ranking Problem on the BrowseGraph

The "Local Ranking Problem" (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8