When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect pr...When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .展开更多
The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects ba...The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.展开更多
Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication...Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.展开更多
目的分析1990—2021年中国5岁以下儿童先天性出生缺陷的发病和疾病负担情况,预测2022—2036年中国5岁以下儿童先天性出生缺陷的发病率,为儿童先天性出生缺陷的预防提供参考。方法利用2021全球疾病负担研究(Global Burden of Disease Stu...目的分析1990—2021年中国5岁以下儿童先天性出生缺陷的发病和疾病负担情况,预测2022—2036年中国5岁以下儿童先天性出生缺陷的发病率,为儿童先天性出生缺陷的预防提供参考。方法利用2021全球疾病负担研究(Global Burden of Disease Study 2021,GBD 2021)数据库,通过发病率和伤残调整生命年(disability-adjusted life year,DALY)等指标描述疾病负担,采用Joinpoint回归模型分析5岁以下儿童先天性出生缺陷发病率及DALY率的变化趋势。采用灰色预测模型GM(1,1)对5岁以下儿童先天性出生缺陷发病率的趋势进行拟合,同时预测2022—2036年5岁以下儿童先天性出生缺陷发病率。结果2021年中国5岁以下儿童先天性出生缺陷发病率为737.28/10万,其中先天性肌肉骨骼/肢体畸形位居发病率首位(307.15/10万),其次为先天性心脏畸形(223.53/10万)、泌尿生殖道先天畸形(74.99/10万)和消化道先天性畸形(62.61/10万)等。1990—2021年中国5岁以下儿童先天性出生缺陷发病率、DALY率分别以平均每年1.73%和5.42%的速度下降。预测结果显示,2022—2036年中国5岁以下儿童先天性出生缺陷发病率呈现降低的趋势:5岁以下儿童先天性出生缺陷发病率从2022年的892.36/10万降低至2036年的783.35/10万。结论1990—2021年中国5岁以下儿童先天性出生缺陷的发病率和疾病负担均呈下降趋势;预测未来至2036年,该发病率将持续降低。展开更多
针对目前含腐蚀缺陷油气管线爆破压力预测方法精度不高问题,基于卷积神经网络方法、双向长短期记忆网络方法及注意力机制构建了CNN-BiLSTM-Attention方法。该方法结合卷积神经网络和双向长短期记忆网络提取含腐蚀缺陷油气管线爆破压力...针对目前含腐蚀缺陷油气管线爆破压力预测方法精度不高问题,基于卷积神经网络方法、双向长短期记忆网络方法及注意力机制构建了CNN-BiLSTM-Attention方法。该方法结合卷积神经网络和双向长短期记忆网络提取含腐蚀缺陷油气管线爆破压力实验数据的参数特征,引入注意力机制关注历史实验数据的特征变化规律,通过全连接层输出爆破压力预测结果。算例分析结果表明,CNN-BiLSTM-Attention方法的平均绝对百分误差和均方根误差分别为0.0420和0.8744,相较其他7种目前常用方法(卷积神经网络方法、长短期记忆网络方法、粒子群算法-反向传播算法、G遗传算法-反向传播算法、ASME B31G-2009标准方法、DNV RP F101-2004标准方法和SHELL92方法),其预测精度明显提高。展开更多
文摘When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .
基金The sustentation fund come fron China Academy of Engineering Physics 2003-421050504-12-02
文摘The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.
文摘Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.
文摘目的分析1990—2021年中国5岁以下儿童先天性出生缺陷的发病和疾病负担情况,预测2022—2036年中国5岁以下儿童先天性出生缺陷的发病率,为儿童先天性出生缺陷的预防提供参考。方法利用2021全球疾病负担研究(Global Burden of Disease Study 2021,GBD 2021)数据库,通过发病率和伤残调整生命年(disability-adjusted life year,DALY)等指标描述疾病负担,采用Joinpoint回归模型分析5岁以下儿童先天性出生缺陷发病率及DALY率的变化趋势。采用灰色预测模型GM(1,1)对5岁以下儿童先天性出生缺陷发病率的趋势进行拟合,同时预测2022—2036年5岁以下儿童先天性出生缺陷发病率。结果2021年中国5岁以下儿童先天性出生缺陷发病率为737.28/10万,其中先天性肌肉骨骼/肢体畸形位居发病率首位(307.15/10万),其次为先天性心脏畸形(223.53/10万)、泌尿生殖道先天畸形(74.99/10万)和消化道先天性畸形(62.61/10万)等。1990—2021年中国5岁以下儿童先天性出生缺陷发病率、DALY率分别以平均每年1.73%和5.42%的速度下降。预测结果显示,2022—2036年中国5岁以下儿童先天性出生缺陷发病率呈现降低的趋势:5岁以下儿童先天性出生缺陷发病率从2022年的892.36/10万降低至2036年的783.35/10万。结论1990—2021年中国5岁以下儿童先天性出生缺陷的发病率和疾病负担均呈下降趋势;预测未来至2036年,该发病率将持续降低。
文摘针对目前含腐蚀缺陷油气管线爆破压力预测方法精度不高问题,基于卷积神经网络方法、双向长短期记忆网络方法及注意力机制构建了CNN-BiLSTM-Attention方法。该方法结合卷积神经网络和双向长短期记忆网络提取含腐蚀缺陷油气管线爆破压力实验数据的参数特征,引入注意力机制关注历史实验数据的特征变化规律,通过全连接层输出爆破压力预测结果。算例分析结果表明,CNN-BiLSTM-Attention方法的平均绝对百分误差和均方根误差分别为0.0420和0.8744,相较其他7种目前常用方法(卷积神经网络方法、长短期记忆网络方法、粒子群算法-反向传播算法、G遗传算法-反向传播算法、ASME B31G-2009标准方法、DNV RP F101-2004标准方法和SHELL92方法),其预测精度明显提高。
