个人简历

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

研究领域

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近期论文

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