Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learn...Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.展开更多
Tobacco leaf shapes including the length,width,area,perimeter and roundness parameters and so on,Only obtain exact boundaries of the leaf information to calculate a large number of leaf parameters.This paper introduce...Tobacco leaf shapes including the length,width,area,perimeter and roundness parameters and so on,Only obtain exact boundaries of the leaf information to calculate a large number of leaf parameters.This paper introduces the classical edge detection Methods,bug method is used to track the boundaries of tobacco leaf extractly.The test shows that the algorithm has a good edge extraction capability.展开更多
以外接激光测距仪的Pioneer3-AT移动机器人作为物理平台,用ARIA-Matlab接口软件实现对物理平台的控制与通信。通过Simulink自定义模块封装Bug算法,设计Matlab Graphical User Interfaces(GUI)界面设置仿真参数和动态显示仿真结果。经由...以外接激光测距仪的Pioneer3-AT移动机器人作为物理平台,用ARIA-Matlab接口软件实现对物理平台的控制与通信。通过Simulink自定义模块封装Bug算法,设计Matlab Graphical User Interfaces(GUI)界面设置仿真参数和动态显示仿真结果。经由笔者开发的折线Bug与圆弧Bug算法实验表明,该软件可灵活执行纯仿真、半实物仿真与物理执行3种工作方式,实验结果与实际情况吻合,验证了Bug避障算法,对经由传感器实时数据采集的路径规划算法研究具有参考意义。展开更多
基金This Research is funded by Researchers Supporting Project Number(RSPD2024R947),King Saud University,Riyadh,Saudi Arabia.
文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
基金Supported by Key Technologies R & D Program of Henan Province(082102210065)Natural Science Research Project of Henan Educational Committee(2007210005)~~
文摘Tobacco leaf shapes including the length,width,area,perimeter and roundness parameters and so on,Only obtain exact boundaries of the leaf information to calculate a large number of leaf parameters.This paper introduces the classical edge detection Methods,bug method is used to track the boundaries of tobacco leaf extractly.The test shows that the algorithm has a good edge extraction capability.
文摘以外接激光测距仪的Pioneer3-AT移动机器人作为物理平台,用ARIA-Matlab接口软件实现对物理平台的控制与通信。通过Simulink自定义模块封装Bug算法,设计Matlab Graphical User Interfaces(GUI)界面设置仿真参数和动态显示仿真结果。经由笔者开发的折线Bug与圆弧Bug算法实验表明,该软件可灵活执行纯仿真、半实物仿真与物理执行3种工作方式,实验结果与实际情况吻合,验证了Bug避障算法,对经由传感器实时数据采集的路径规划算法研究具有参考意义。
