The visual monitoring performance of teleoperated robots in complex environments has always been challenging, and existing viewpoint selection methods such as free and first-person viewpoints are inadequate. The free viewpoint provides a high degree of control freedom but increases the cognitive load of the operator, while the first-person viewpoint has a limited field of view, making it difficult to obtain a global perception. In this paper, we propose an automatic viewpoint selection method based on artificial potential field (APF). By setting gravity and repulsion fields around important points and obstacles, the camera robot can adaptively adjust its viewpoint to ensure that the operator always obtains the best view for task execution. Experiments were designed for a mobile robot navigating a sanitization task, and the method was compared with existing methods. The results show that the proposed method excels in objective indicators such as task completion time, while significantly reducing the operator's emotional fluctuation and cognitive load, providing technical support for realizing efficient, comfortable, and low-risk teleoperation.

How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. The model uses code metrics to score software reliability. This evaluation technique requires the aggregation of software code metrics into project metrics. What are the differences among different aggregation methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability evaluation model we have established are our concerns. In view of the above problems, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects.

This paper proposes an empirical study of exploring relevant and operable metrics to assess software product quality based on quality characteristics of ISO/IEC 25010. Both data-driven and Goal-Question-Metric approach are applied to excavate as many full-life-cycle metrics as possible. Goal-Question-Metric approach is applied to identify software metrics affecting product quality in a specific domain. Machine learning algorithm that support incremental training is applied to learn the relationship between code metrics and quality characteristic marks from historical data. Thus full-life-cycle software metrics are identified and we build a software quality assessment model based on historical code metric data.Two case studies are conducted based on actual projects from the past 2 years to verify the feasibility of methodology, including an empirical investigation of mostly concerned quality factors in an electronic equipment software institute, a collection of code metrics and quality characteristic marks of 82 aviation embedded software projects, as well as a comparation of the performance of logistic regression, k-Nearest Neighbor and BP neural network algorithm for quality assessment. Those cases are performed with actual data in actual institutes and projects. Additionally, a support tool for project data management and quality assessment is developed.Through feedback from cooperative engineers, we still see room for improvements to fill the gap between methodology and actual software engineering process. Nevertheless, the empirical validation shows the feasibility of the metrics for quality assessment.

How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. This evaluation technique requires the aggregation of software code metrics into project metrics. Statistical value methods, metric distribution methods, and econometric methods are commonly-used aggregation methods. What are the differences between these methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability assessment model we have established are our concerns. In view of these concerns, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects. We find the distribution of code metrics for the projects under study. Using these distribution laws, we optimize the aggregation method of code metrics and improve the accuracy of the software reliability evaluation model.

This paper proposes a method to evaluate software quality by learning from historical data. Quantitative evaluation of software quality is not an easy issue. But historical software can provide us with a lot of software quality information. We present a data acquiring model to guide the data collection from historical software. Then machine learning algorithm that support incremental training is applied to learn the relationship between software quality and software metrics from the data. As a case study, we collected the data of 82 aviation embedded software in an institute, and trained a k-Nearest Neighbors (k-NN) classification model optimized by genetic algorithms for evaluating the software reliability.