Automatic assignment of integrators to pull requests: The importance of selecting appropriate attributes

In open-source projects that adopt the pull-based development workflow, a core developer needs to analyze the contribution received via pull requests and decide on integrating it or not in the repository. However, this process is time-consuming, leading to an increasing number of pull requests left to be analyzed. Consequently, the assignment of suitable integrators to pull requests becomes an important step in the pull-based development workflow. Classification methods have already been used to recommend integrators, based on different sets of predictive attributes. The main contribution of this paper is to identify a set of attributes that can improve the performance of the integrator prediction task reported in the literature. To do so, we first evaluate different sets of attributes used by previous studies with different classification algorithms. Besides, we explore attribute selection strategies on an extended set of attributes composed not only by the attributes already used in the literature but also new attributes we consider relevant to the problem. Experiments with 32 open-source projects evidenced that after applying attribute selection strategies and, consequently, identifying a more suitable set of attributes, the recommendation has achieved normalized improvements 54% higher than the state-of-the-art.