  With the increase of complexity of modern software, social collaborative
coding and reuse of open source software packages become more and more popular,
which thus greatly enhances the development efficiency and software quality.
However, the explosive growth of open source software packages exposes
developers to the challenge of information overload. While this can be
addressed by conventional recommender systems, they usually do not consider
particular constraints of social coding such as social influence among
developers and dependency relations among software packages. In this paper, we
aim to model the dynamic interests of developers with both social influence and
dependency constraints, and propose the Session-based Social and
Dependency-aware software Recommendation (SSDRec) model. This model integrates
recurrent neural network (RNN) and graph attention network (GAT) into a unified
framework. An RNN is employed to model the short-term dynamic interests of
developers in each session and two GATs are utilized to capture social
influence from friends and dependency constraints from dependent software
packages, respectively. Extensive experiments are conducted on real-world
datasets and the results demonstrate that our model significantly outperforms
the competitive baselines.
