Discovering stakeholders' interests in Wiki-based architectural documentation
|Discovering stakeholders' interests in Wiki-based architectural documentation|
|Author(s)||Nicoletti M., Diaz-Pace J.A., Schiaffino S.|
|Published in||CIbSE 2013: 16th Ibero-American Conference on Software Engineering - Memorias de la 16th Conferencia Iberoamericana de Ingenieria de Software, CIbSE 2013|
|Keyword(s)||Architectural documentation, Software architecture, Stakeholders, Text mining, User profiling, Wikis (Extra: Communication and interaction, Information overloads, NAtural language processing, Stakeholders, Text mining, User profiling, User profiling technique, Wikis, Data mining, Software architecture, Natural language processing systems)|
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Discovering stakeholders' interests in Wiki-based architectural documentation is a 2013 conference paper written in English by Nicoletti M., Diaz-Pace J.A., Schiaffino S. and published in CIbSE 2013: 16th Ibero-American Conference on Software Engineering - Memorias de la 16th Conferencia Iberoamericana de Ingenieria de Software, CIbSE 2013.
The Software Architecture Document (SAD) is an important artifact in the early stages of software development, as it serves to share and discuss key design and quality-attribute concerns among the stakeholders of the project. Nowadays, architectural documentation is commonly hosted in Wikis in order to favor communication and interactions among stakeholders. However, the SAD is still a large and complex document, in which stakeholders often have difficulties in finding information that is relevant to their interests or daily tasks. We argue that the discovery of stakeholders' interests is helpful to tackle this information overload problem, because a recommendation tool can leverage on those interests to provide each stakeholder with SAD sections that match his/her profile. In this work, we propose an approach to infer stakeholders' interests, based on applying a combination of Natural Language Processing and User Profiling techniques. The interests are partially inferred by monitoring the stakeholders' behavior as they browse a Wiki-based SAD. A preliminary evaluation of our approach has shown its potential for making recommendations to stakeholders with different profiles and support them in architectural tasks.
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