Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
Digital mine is the inevitable outcome of the information processing, and is also a complicated system engineering. Firstly, for the 3D visualization application of the digital mine, the ground and underground integra...Digital mine is the inevitable outcome of the information processing, and is also a complicated system engineering. Firstly, for the 3D visualization application of the digital mine, the ground and underground integrative visualization framework model was proposed based on the mine entity database. So, the visualization problem was availably resolved, as well as the professional analytical ability was improved. Secondly, aiming at the irregularities, non-uniformity, dynamics of mine entities, mix modeling method based on the entity character was put forward, in which 3D expression of mine entities was realized. Lastly, the 3D visualization project for a copper mine was experimentally studied. Satisfactory results were acquired, and the rationality of visualization model and feasibility of 3D modeling were validated.展开更多
An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, clo...An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.展开更多
实时获取农作物长势及产量等信息对于现代农业的发展具有重要意义。近年来,随着遥感技术(remote sensing,RS)和地理信息系统(geographic information system,GIS)广泛应用于农作物估产领域,相继出现了一些较为实用的估产方法,主要有结...实时获取农作物长势及产量等信息对于现代农业的发展具有重要意义。近年来,随着遥感技术(remote sensing,RS)和地理信息系统(geographic information system,GIS)广泛应用于农作物估产领域,相继出现了一些较为实用的估产方法,主要有结合辅助数据的估产方法、基于植被指数的估产方法、基于特定模型的估产方法和基于农作物估产平台(软件)的开发等。其中,基于植被指数的估产方法又分为单一和多植被指数估产2类方法。在对近年来该领域大量文献深入研究的基础上,着重就几类热点方法展开论述,并对每类方法的优势和缺陷进行了评述,最后对该领域需要进一步研究的方向进行了探讨和展望,以期为后续研究提供参考。展开更多
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
基金Project(41061043)supported by the National Natural Science Foundation of China
文摘Digital mine is the inevitable outcome of the information processing, and is also a complicated system engineering. Firstly, for the 3D visualization application of the digital mine, the ground and underground integrative visualization framework model was proposed based on the mine entity database. So, the visualization problem was availably resolved, as well as the professional analytical ability was improved. Secondly, aiming at the irregularities, non-uniformity, dynamics of mine entities, mix modeling method based on the entity character was put forward, in which 3D expression of mine entities was realized. Lastly, the 3D visualization project for a copper mine was experimentally studied. Satisfactory results were acquired, and the rationality of visualization model and feasibility of 3D modeling were validated.
基金Project(51274250)supported by the National Natural Science Foundation of ChinaProject(2012BAK09B02-05)supported by the National Key Technology R&D Program during the 12th Five-year Plan of China
文摘An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.
文摘实时获取农作物长势及产量等信息对于现代农业的发展具有重要意义。近年来,随着遥感技术(remote sensing,RS)和地理信息系统(geographic information system,GIS)广泛应用于农作物估产领域,相继出现了一些较为实用的估产方法,主要有结合辅助数据的估产方法、基于植被指数的估产方法、基于特定模型的估产方法和基于农作物估产平台(软件)的开发等。其中,基于植被指数的估产方法又分为单一和多植被指数估产2类方法。在对近年来该领域大量文献深入研究的基础上,着重就几类热点方法展开论述,并对每类方法的优势和缺陷进行了评述,最后对该领域需要进一步研究的方向进行了探讨和展望,以期为后续研究提供参考。
