  The use of relevant metrics of software systems could improve various
software engineering tasks, but identifying relationships among metrics is not
simple and can be very time consuming. Recommender systems can help with this
decision-making process, many applications have utilized these systems to
improve the performance of their applications. To investigate the potential
benefits of recommender systems in regression testing, we implemented an
item-based collaborative filtering recommender system that uses user
interaction data and application change history information to develop a test
case prioritization technique. To evaluate our approach, we performed an
empirical study using three web applications with multiple versions and
compared four control techniques. Our results indicate that our recommender
system can help improve the effectiveness of test prioritization.
