The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods hav...The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can </span><span style="font-family:Verdana;">apply to the system testing phase of software development. On the other</span><span style="font-family:Verdana;"> hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.展开更多
A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively rem...A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.展开更多
文摘The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can </span><span style="font-family:Verdana;">apply to the system testing phase of software development. On the other</span><span style="font-family:Verdana;"> hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.
基金Supported by the National Natural Science Foundation of China(21576143).
文摘A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.
