4.7 Article

Session-based social and dependency-aware software recommendation

Journal

APPLIED SOFT COMPUTING
Volume 118, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.asoc.2022.108463

Keywords

Software recommendation; Social network; Dependency network; Graph neural network

Funding

  1. National Key Research and Development Program of China [2019YFB1704101]
  2. Na-tional Natural Science Foundation of China [61872002, U1936220]
  3. University Natural Science Research Project of Anhui Province [KJ2019A0037]

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With the increase in complexity of modern software, social collaborative coding and reuse of open source software packages have become popular. This paper proposes a Session-based Social and Dependency-aware software Recommendation (SSDRec) model that considers social influence and dependency constraints. Extensive experiments demonstrate the superiority of the model.
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. (c) 2022 Elsevier B.V. All rights reserved.

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