邱少健 - 华南农业大学 - 数学与信息学院、软件学院

个人简介

邱少健，华南农业大学数学与信息（软件）学院教师，IEEE、CCF会员，CCF YOCSEF广州委员。近五年从事智能软件工程、迁移学习领域研究。发表或参与发表国际SCI刊物论文、国际会议论文和中文核心刊物论文20余篇；申请或授权发明专利4件；主持广东省普通高校青年创新人才项目1项，参与国家自然科学基金、广东省应用型重大专项和省自然科学基金各1项，参与项目“移动互联网软件用户行为分析与可靠性保障平台建设及规模化应用”获2019年度广东省科技进步奖二等奖。知识产权 [1] 发明专利（ZL201610298856.0）. 一种基于用户频繁访问序列模型的Web应用性能测试方法. 2018.10 . 陆璐; 邱少健 [2] 发明专利（CN201810743379.3）. 一种基于卷积神经网络的软件缺陷预测方法. 2018.07 . 陆璐; 邱少健项目列表 [1] 广东省普通高校青年创新人才项目（2020KQNCX008）. 基于代码语义特征的软件缺陷预测技术研究. 2021.01-2022.12. 主持

近期论文

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期刊论文 2021 [1] Zou, Quanyi, Lu, Lu, Yang, Zhanyu, Gu, Xiaowei, Qiu, Shaojian. Joint feature representation learning and progressive distribution matching for cross-project defect prediction. Information and Software Technology, 2021 [2] Zou, Quanyi, Lu, Lu, Qiu, Shaojian, Gu, Xiaowei, Cai, Ziyi. Correlation feature and instance weights transfer learning for cross project software defect prediction. IET Software, 2021, 15(1): 55-74 2020 [3] 欧阳鹏, 陆璐, 张凡龙, 邱少健. 基于迁移学习和过采样技术的跨项目克隆代码一致性维护需求预测. 计算机科学, 2020, 47(09): 16-22 [4] Gu, Xiaowei, Lu, Lu, Qiu, Shaojian, Zou, Quanyi, Yang, Zhanyu. Sentiment key frame extraction in user-generated micro-videos via low-rank and sparse representation. Neurocomputing, 2020, 410: 441-453 [5] Deng, Jiehan, Lu, Lu, Qiu, Shaojian. Software defect prediction via LSTM. IET Software, 2020, 14(4): 443-450 [6] Deng, Jiehan, Lu, Lu, Qiu, Shaojian, Ou, Yangpeng. A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction. IEEE Access, 2020, 8: 66647-66661 2019 [7] 邱少健, 蔡子仪, 陆璐*. 基于卷积神经网络的代价敏感软件缺陷预测模型. 计算机科学, 2019, 46(11): 156-160 [8] Cai, Ziyi, Lu, Lu, Qiu, Shaojian. An abstract syntax tree encoding method for cross-project defect prediction. IEEE Access, 2019, 7: 170844--170853 [9] Qiu, Shaojian, Xu, Hao, Deng, Jiehan, Jiang, Siyu, Lu, Lu. Transfer convolutional neural network for cross-project defect prediction. Applied Sciences, 2019, 9(13): 2660 [10] Qiu, Shaojian, Lu, Lu, Jiang, Siyu. Joint distribution matching model for distribution--adaptation-based cross-project defect prediction. IET Software, 2019, 13(5): 393-402 [11] 姜思羽, 钟晓玲, 邱少健, 宋恒杰. 结合标签相关性和不均衡性的多标签学习模型. 哈尔滨工业大学学报, 2019, 51(01): 142-149 [12] Qiu, Shaojian, Lu, Lu, Jiang, Siyu, Guo, Yang. An investigation of imbalanced ensemble learning methods for cross-project defect prediction. IJPRAI, 2019, 33(12): 1959037 [13] Jiang, Siyu, Xu, Yonghui, Wang, Tengyun, Yang, Haizhi, Qiu, Shaojian, Yu, Han, Song, Hengjie. Multi-label metric transfer learning jointly considering instance space and label space distribution divergence. IEEE Access, 2019, 7: 10362-10373 2018 [14] Jiang, Siyu, Xu, Yonghui, Song, Hengjie, Wu, Qingyao, Ng, Michael K, Min, Huaqing, Qiu, Shaojian. Multi-instance transfer metric learning by weighted distribution and consistent maximum likelihood estimation. Neurocomputing, 2018, 321: 49--60 [15] Qiu, Shaojian, Lu, Lu, Jiang, Siyu. Multiple-components weights model for cross-project software defect prediction. IET Software, 2018, 12(4): 345-355 2014 [16] 邱少健, 姜思羽, 刘爱实, 杨剑, 黄楚然. 嵌入式可视门禁系统的设计方法. 重庆大学学报, 2014, 37(6): 78-82 [17] 姜思羽, 吴斌, 邱少健, 羊梅君. 驾驶员疲劳检测技术的算法设计与硬件实现. 哈尔滨工业大学学报, 2014, 46(5): 95-100 会议论文 2019 [1] Qiu, Shaojian, Lu, Lu, Cai, Ziyi, Jiang, Siyu. Cross-Project Defect Prediction via Transferable Deep Learning-Generated and Handcrafted Features.. SEKE, 2019: 431-552