Recommending good first issues in GitHub OSS projects

Xiao, Wenlei; He, Hao; Xu, Weiwei; Tan, Xin; Dong, Jinhao; Zhou, Minghui

doi:10.1145/3510003.3510196

Cited by 19 publications

(1 citation statement)

References 60 publications

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“…Methodological studies explore project classification [66], code search [31], connecting publications with projects [40]. Although previous works have explored the recommendation task in OSS development settings such as automatic suggestions of API function calls [19,37], Good First Issues [1,53], and data preparation steps [57], no previous works have studied the challenging task of code recommendation task, which requires in-depth understanding of OSS projects written in multiple programming languages and diverse user-item interactions.…”

Section: Related Work 51 Research In Open Sourcementioning

confidence: 99%

Code Recommendation for Open Source Software Developers

Jin¹,

Bai²,

Zhu³

et al. 2022

Preprint

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Open Source Software (OSS) is forming the spines of technology infrastructures, attracting millions of talents to contribute. Notably, it is challenging and critical to consider both the developers' interests and the semantic features of the project code to recommend appropriate development tasks to OSS developers. In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects. Considering the complex interactions among multiple parties within the system, we propose CODER, a novel graph-based code recommendation framework for open source software developers. CODER jointly models microscopic user-code interactions and macroscopic userproject interactions via a heterogeneous graph and further bridges the two levels of information through aggregation on file-structure graphs that reflect the project hierarchy. Moreover, due to the lack of reliable benchmarks, we construct three large-scale datasets to facilitate future research in this direction. Extensive experiments show that our CODER framework achieves superior performance under various experimental settings, including intra-project, crossproject, and cold-start recommendation. We will release all the datasets, code, and utilities for data retrieval upon the acceptance of this work.

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