Session-Based Sofware Recommendation with Social and Dependency Graph DENGCHENG YAN, Anhui University, China TIANYI TANG, Anhui University, China YIWEN ZHANG ∗ , Anhui University, China Reusing mature software packages that have been developed repeatedly can greatly enhance the efciency and quality of software development. However, with the rapidly growing number of software packages, developers are facing the challenge on technology choices. In this context, software recommendation plays a crucial role in software development. While conventional recommendation models can be applied to software recommendation, regrading to the unique characteristics of software development, there still remains three challenges: 1) developers’ interests are gradually evolving, 2) developer are infuenced by their friends, and 3) software packages are infuenced by their dependency. Notably, the social infuences are dynamic and the dependency infuences are attentive. That is, developers may trust diferent sets of friends at diferent times and diferent dependency exhibits diferent importance. In this paper, we propose a novel software recommendation model, named as Session-based Social and Dependence-aware Recommendation (SSDRec). It integrates recurrent neural network (RNN) and graph attention network (GAT) into a unifed framework. This model employs RNN on short session-based data to model developers’ evolving interests. In addition, we extend GAT to Social-Dependency-GAT (SD-GAT) for modeling both dynamic social infuences and attentive dependency infuences. Extensive experiments are conducted on real-world datasets and the results demonstrate the advantages of our model over state-of-the-art methods for modeling developers’ evolving interests and the two infuences. CCS Concepts: · Computing methodologies → Neural networks; · Software and its engineering → Software libraries and repositories;· Information systems → Social recommendation. Additional Key Words and Phrases: Software recommendation, Social network, Dependency network, Graph neural network ACM Reference Format: Dengcheng Yan, Tianyi Tang, and Yiwen Zhang. 2021. Session-Based Software Recommendation with Social and Dependency Graph. 1, 1 (November 2021), 17 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn 1 INTRODUCTION The rapid development of the Internet has signifcantly fueled the prosperity of the software industry. According to AppBrain reports, in Q2, 2019 alone, over 2 million software was available on Google Play. Developers often need to develop high-quality software projects in a short time. ∗ Corresponding author. Authors’ addresses: Dengcheng Yan, yanzhou@ahu.edu.cn, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Rd, Hefei, Anhui, China; Tianyi Tang, tangtianyi202012@163.com, School of Computer Science and Technology, Anhui University, 111 Jiulong Rd, Hefei, Anhui, China; Yiwen Zhang, zhangyiwen@ahu.edu.cn, School of Computer Science and Technology, Anhui University, 111 Jiulong Rd, Hefei, Anhui, China. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. XXXX-XXXX/2021/11-ART $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn , Vol. 1, No. 1, Article . Publication date: November 2021. arXiv:2103.06109v1 [cs.IR] 10 Mar 2021