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publications
Enhancing the Performance of Next-Generation SSD Arrays: A Holistic Approach
Published in ACM Transactions on Storage, 2025
All-flash arrays (AFAs) face software overheads that limit the performance of next-generation SSDs. We present ScalaAFA, a user-space AFA engine that reduces these overheads by offloading internal tasks to SSDs and using efficient inter-thread synchronization. With novel data placement and careful exploitation of SSD features, ScalaAFA achieves up to 2.5× higher write throughput and 52.7% lower write latency than state-of-the-art approaches.
Recommended citation: Jie Zhang, Shushu Yi, Xiurui Pan, Yiming Xu, Qiao Li, Qiang Li, Chenxi Wang, Bo Mao, and Myoungsoo Jung. 2025. Enhancing the Performance of Next-Generation SSD Arrays: A Holistic Approach. ACM Trans. Storage Just Accepted (May 2025). https://doi.org/10.1145/3736588
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Predicting Abandonment of Open Source Software Projects with An Integrated Feature Framework
Published in Arxiv, 2025
Open Source Software (OSS) is a cornerstone of contemporary software development, yet the increasing prevalence of OSS project abandonment threatens global software supply chains. Although previous research has explored abandonment prediction methods, these methods often demonstrate unsatisfactory predictive performance, further plagued by imprecise abandonment discrimination, limited interpretability, and a lack of large, generalizable datasets. In this work, we address these challenges by reliably detecting OSS project abandonment through a dual approach: explicit archival status and rigorous semantic analysis of project documentation or description. Leveraging a precise and scalable labeling pipeline, we curate a comprehensive longitudinal dataset of 115,466 GitHub repositories, encompassing 57,733 confirmed abandonment repositories, enriched with detailed, timeline-based behavioral features. Building on this foundation, we introduce an integrated, multi-perspective feature framework for abandonment prediction, capturing user-centric, maintainer-centric, and project evolution features. Survival analysis using an AFT model yields a high C-index of 0.846, substantially outperforming models confined to surface features. Further, feature ablation and SHAP analyses confirm both the predictive power and interpretability of our approach. We further demonstrate practical deployment of a GBSA classifier for package risk in openEuler. By unifying precise labeling, multi-perspective features, and interpretable modeling, our work provides reproducible, scalable, and practitioner-oriented support for understanding and managing abandonment risk in large OSS ecosystems. Our tool not only predicts abandonment but also enhances program comprehension by providing actionable insights into the health and sustainability of OSS projects.
Recommended citation: Yiming Xu and Runzhi He and Hengzhi Ye and Minghui Zhou and Huaimin Wang:Predicting Abandonment of Open Source Software Projects with An Integrated Feature Framework.[J].arXiv preprint arXiv:2507.21678, 2025.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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