Improving Change Recommendation using Aggregated Association Rules

Past research has proposed association rule mining as a means to uncover the evolutionary coupling from a system's change history. These couplings have various applications, such as improving system decomposition and recommending related changes during development. The strength of the coupling can be characterized using a variety of interestingness measures. Existing recommendation engines typically use only the rule with the highest interestingness value in situations where more than one rule applies. In contrast, we argue that multiple applicable rules indicate increased evidence, and hypothesize that the aggregation of such rules can be exploited to provide more accurate recommendations.To investigate this hypothesis we conduct an empirical study on the change histories of two large industrial systems and four large open source systems. As aggregators we adopt three cumulative gain functions from information retrieval. The experiments evaluate the three using 39 different rule interestingness measures. The results show that aggregation provides a significant impact on most measure's value and, furthermore, leads to a significant improvement in the resulting recommendation.