电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先,该方法从多个维度选取电商财务风险相关指标,以全面反映电商企业的财务健康状况。随后,对选取的指标数据进行预处理,确保数据质量和模型训练的准确性。接下来,建立一个改进的BP神经网络模型,用于电商财务风险的预测。在模型建立过程中,特别关注学习速率的调整,通过改变学习率来平衡模型的训练速度和稳定性,从而实现财务风险预测。实验结果表明:基于改进BP神经网络的电商财务风险智能预测方法实现了每2 min更新一次的高频率,其平均更新时间仅为1 s左右,更新成功率稳定在99%以上,在更新能力方面表现优秀,可为电商企业的财务风险预测提供新的解决方案。The financial data of e-commerce enterprises usually involve a large number of transactions and complex business logic, and the collection, cleaning and sorting of data require a lot of time and manpower, resulting in a low frequency of data update, which limits the accuracy of financial risk prediction model. Therefore, this paper studies the intelligent prediction method of financial risk of e-commerce based on improved BP neural network. First, the method selects indicators related to e-commerce financial risks from multiple dimensions to comprehensively reflect the financial health of e-commerce enterprises. Then, the selected index data is preprocessed to ensure the data quality and the accuracy of model training. Next, an improved BP neural network model is established to predict the financial risk of e-commerce. In the process of model building, we pay special attention to the adjustment of learning rate, and balance the training speed and stability of the
回归测试用例选择(Regression Test Case Selection,RTS)问题是回归测试研究中的一个热点,旨在从已有测试用例集中选择出所有可检测代码修改的测试用例。但迄今为止,国内研究人员并未对RTS问题的已有研究成果进行系统总结和比较。首先在回归测试活动和测试用例划分基础上,引出RTS问题和相关假设。随后从源代码和模型角度对已有RTS技术进行分类,从源代码角度出发,又进一步将其细分为线性规划法、数据流分析法、图遍历法、程序切片法和防火墙法等。接着对常见评测数据集和评测指标进行总结,最后对该问题的未来研究方向进行了展望。
