Tag: recommendation systems
Recommendation systems “provide suggestions for items that are of potential interest for a user” (Felfernig et al., 2014). The two main techniques to match users and items are content-based filtering and collaborative filtering. Content-based filtering finds patterns in the content of items that have been rated by a user, to find new items that are likely to match his interests. Collaborative filtering identifies users that display similar preference patterns. Many recommendation systems also combine the two techniques in hybrid systems. Robillard et al. (2010) have proposed a dedicated definition of Recommendation Systems for Software Engineering (RSSE): “a software application that provides information items estimated to be valuable for a software engineering task in a given context”. RSSE research was a fundamental part of my PhD thesis and the tool ImpRec, an RSSE for change impact analysis.
Supporting Change Impact Analysis Using a Recommendation System: An Industrial Case Study in a Safety-Critical Context
TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R
Navigating Information Overload Caused by Automated Testing – A Clustering Approach in Multi-Branch Development
The More the Merrier: Leveraging on the Bug Inflow to Guide Software Maintenance
