Abstract
Open source software (OSS) development has become a trend and many popular software systems are developed and maintained by open source communities. In open source communities, developers are loosely organized which brings difficulties to the management of OSS projects. The events happening in the open source community may affect the development of the OSS project, which should be taken care of by the project organizers. Unfortunately, to identify these events from large amounts of messages generated in the community, especially from multiple sources, is still a big challenge. In this paper, a Domain-embedding-based Open-source Community Event Monitoring Model (DOCEM) is proposed to identify events from multiple sources. Specifically, DOCEM is based on DualGAN and StarGAN. An event dataset for Tensorflow project is constructed to train and test DOCEM. The experiment results show that DOCEM has much better performance than counterparts.
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Acknowledgement
This work is partially supported by National Key Research and Development Plan (No. 2018YFB1003800) and China National Science Foundation (Granted Number 62072301).
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Huang, H., Cao, J., Qi, Q., Zhao, B. (2022). DOCEM: A Domain-Embedding-Based Open-Source Community Event Monitoring Model. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1492. Springer, Singapore. https://doi.org/10.1007/978-981-19-4549-6_31
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