Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.

A monotone drawing of a graph $G$ is a straight-line drawing of $G$ such that, for every pair of vertices $u,w$ in $G$, there exists a path $P_{uw}$ in $G$ that is monotone in some direction $l_{uw}$. (Namely, the order of the orthogonal projections of the vertices of $P_{uw}$ on $l_{uw}$ is the same as the order in which they appear in $P_{uw}$.) The problem of finding monotone drawings for trees has been studied in several recent papers. The main focus is to reduce the size of the drawing. Currently, the smallest drawing size is $O(n\log n) \times O(n\log n)$. In this paper, we present an algorithm for constructing monotone drawings of trees on a grid of size at most $12n \times 12n$. The smaller drawing size is achieved by a procedure that carefully assigns primitive vectors to the paths of the input tree $T$. We also show that there exists a tree $T_0$ such that any monotone drawing of $T_0$ must use a grid of size at least $n/9 \times n/9$. So the size of our monotone drawings is asymptotically optimal.

In this paper, we propose a novel collaborative filtering approach for predicting the unobserved links in a network (or graph) with both topological and node features. Our approach improves the well-known compressed sensing based matrix completion method by introducing a new multiple-independent-Bernoulli-distribution model as the data sampling mask. It makes better link predictions since the model is more general and better matches the data distributions in many real-world networks, such as social networks like Facebook. As a result, a satisfying stability of the prediction can be guaranteed. To obtain an accurate multiple-independent-Bernoulli-distribution model of the topological feature space, our approach adjusts the sampling of the adjacency matrix of the network (or graph) using the clustering information in the node feature space. This yields a better performance than those methods which simply combine the two types of features. Experimental results on several benchmark datasets suggest that our approach outperforms the best existing link prediction methods.