An Approach to Detect Structural Development Defects in Object-Oriented Programs

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.

An Edge-assisted, Object-oriented Random Forest Approach for Refined Extraction of Tea Plantations Using Multi-temporal Sentinel-2 and High-resolution Gaofen-2 Imagery

As a consumed and influential natural plant beverage,tea is widely planted in subtropical and tropical areas all over the world.Affected by(sub)tropical climate characteristics,the underlying surface of the tea distribution area is extremely complex,with a variety of vegetation types.In addition,tea distribution is scattered and fragmentized in most of China.Therefore,it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods.This study proposed a boundary-enhanced,object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data.This method uses multispectral indexes,textures,vegetable indices,and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations.To reduce feature redundancy and computation time,the feature elimination algorithm based on Mean Decrease Accuracy(MDA)was used to generate the optimal feature set.Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types,high resolution GF-2 image was segmented based on the MultiResolution Segmentation(MRS)algorithm to assist the segmentation of Sentinel-2,which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations.Finally,the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain,Yunnan Province.The resulting tea plantation map had high accuracy,with a 95.38%overall accuracy and 0.91 kappa coefficient.We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.

Object-Oriented Modeling of the Variation of Acceleration and Deceleration Characteristics in Relation to Speed Bands for Railway Vehicles

Automated operation and artificial intelligence technology have become essential for ensuring the safety, efficiency, and punctuality of railways, with applications such as ATO (Automatic Train Operation). In this study, the authors propose a method to efficiently simulate the kinematic characteristics of railroad vehicles depending on their speed zone. They utilized the function overloading function supported by a programming language and applied the fourth-order Lunge-Kutta method for dynamic simulation. By constructing an object model, the authors calculated vehicle characteristics and TPS and compared them with actual values, verifying that the developed model represents the real-life vehicle characteristics accurately. The study highlights potential improvements in automated driving and energy consumption optimization in the railway industry.

Exploration on 5E Teaching Principle-based Education for the Object-oriented Software Construction Course

Most current object-oriented programming courses offered by domestic colleges and universities generally focus on the object-oriented programming language itself,i.e.,the programming grammar of the language,but ignore the design pattern.However,design patterns are essential to software engineering because they can solve common problems in software design and improve code reuse,readability,extensibility,and reliability.Our Object-oriented Software Construction Course is creative since it aims at cultivating students’object-oriented thinking as well as basic abilities required to construct high-quality,object-oriented software.Specifically,we exploit the 5E teaching principle during the education of this course,and present the whole pipeline in the paper.We also provide one case of the factory pattern to further demonstrate the implementation of the 5E teaching principle in the course.The effect of the 5E teaching principle has also been demonstrated.

基于XML的财经类跨专业教学综合实训系统设计

为了解决现有财经类跨专业教学系统中存在的实习边缘化、专业知识运用不到位、经营流程片面化等问题,提出设计基于XML的财经类跨专业教学综合实训系统。在XML基础上建立模拟真实企业环境的实训系统,系统中硬件部分由管理员子系统、教师子系统和学生子系统组成,分别为管理员、教师和学生提供了管理、查找、下载等功能;软件部分设计多个模块,为学生和教师提供查看和发布公告、答疑、个人信息修改和实训任务等功能。采用层次分析法,计算各个指标之间权重,评判系统的优劣性。实验结果结果表明系统性能较高,具有一定可行性。

基于C#的卫星影像元数据(XML)软件设计与开发

元数据定义为描述数据的数据,是对数据及信息资源的描述性信息。此软件的设计与开发能够提取源数据中的信息,减少人为中间数据处理的粗差,提供模板批量生成机制,提高效率与准确率,为DLG、DOM、DEM、DSM等元数据或其他需要模板机制生成的数据提供了批量生产的可行性。

Performance Enhancement of XML Parsing Using Regression and Parallelism

The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

基于XML的勘查技术方法标准数字化研究探讨——以《大地电磁测深法技术规程(DZ/T 0173-2022)》为例

【研究目的】作为地质行业发展的重要技术保障,勘查技术方法标准的精细化和综合化的发展趋势在客观上迫切需要加快存量标准的数字化,以满足标准技术内容的快速检索、分析、比对、拆分与重构等诸多需求。【研究方法】以勘查技术方法标准体系中占比最大、结构趋同的规程类型标准《大地电磁测深法技术规程(DZ/T 0173-2022)》为例,参照国家标准结构化置标框架,采用可扩展标记语言XML对其进行结构化。【研究结果】建立了勘查技术规程类型标准要素与结构化框架的对应关系,基于XML实现了勘查技术方法标准的基本信息、辅助信息、术语信息和主体内容等模块的结构化描述,提出了结构化框架存在要素缺失、结构化描述维度单一、结构化模块颗粒度较粗等不足。【结论】勘查技术方法标准数字化研究应加强开展不同功能类型标准结构元模型、元素集、内容模板和样式表等方面研究,实现全要素、多维度、细颗粒的标准内容结构化,为实现勘查技术方法标准机器可读、可解析奠定坚实基础。

基于Office Open XML的Excel操作题自动阅卷系统的实现

Excel操作题自动阅卷系统的设计往往采用RTF、VBA方式,但效果并不理想,针对这种情况提出了一种基于Office Open XML格式和Python语言,使用开源库Openpyxl、Element Tree及自编类解析Excel文件的操作题自动阅卷评分系统的方法,总结了Office Open XML格式的Spread sheet ML标记语言的常用标记及作用,其次设计了自动阅卷流程,细化为格式解析、格式保存和格式对比量化评分三个阶段,并将格式解析分解为Openpyx l解析、Element Tree解析两个步骤,解决了解析中容错的自定义问题。经过测试,使用该方法能可靠地实现Excel 2010版本操作题的自动阅卷。

基于XML技术的Web数据库应用系统设计

随着互联网的普及与发展,数据库技术已成为支撑各类应用系统的核心。然而,传统的关系型数据库在跨平台的数据交换、异构系统间的信息共享等应用场景中存在诸多局限。在这一背景下,XML技术应运而生,其作为一种数据描述和交换的标准,为Web数据库应用系统的设计提供了新的思路和解决方案。文中介绍了XML技术的特点、XML结合Web数据库的优势,分析了基于XML技术的Web数据库应用系统的设计与实现,以期为数据的存储、查询和管理提供参考。

基于XML技术的电子信息资源数据库智能检索方法

为提高检索内容与关键词的匹配度,本文引进可扩展标记语言(extensible markup language,XML)技术,开展电子信息资源数据库智能检索方法的设计研究。首先,根据XML技术中的标记工具和元数据,对电子信息资源进行描述和组织,同时利用XML的查询语言和解析器,对数据库中电子资源进行语义标注;其次,建立电子信息资源数据库中的元信息索引,抽取元信息,将抽取的元信息存储到一个表格中,表格内容可以根据需要随时扩展和更新;最后,一旦确定了检索条件并输入,系统便能实现对数据库中文档资源的快速检索。将本文设计方法与传统方法进行对比,实验结果表明:规范使用设计方法进行智能检索,可扩大检索范围、增加展示数据条数,并提升内容与关键词匹配度,满足用户检索需求,实现高效、精确的信息检索方法设计与开发。

基于XML的简易运动员信息管理系统的设计与实现研究

研究目的:数字化时代,运动队建设应该采用信息化管理手段。运动员信息是运动队管理的基本内容,开发简易的运动员信息管理系统可以让管理工作从繁重的人工作业中解脱出来,并促进形成系统规律的、层级明显的新型管理模式。研究方法:采用文献资料法、实验法设计与实现简易运动员信息管理系统。结果表明:该系统可以解决并处理运动队建设过程中运动员个人信息的数字化管理工作难题,通过对数据的录入、归档、检查、整理,可以实现数据的永久存储、快速更新、多重查阅等功能。同时,该系统降低了人工操作的繁琐性及误差率,提高了数据管理的工作效率,体现了数字化工作对运动队建设与发展的推动作用。结论:选择XML文档作为运动员信息管理系统存储数据介质有明显的层级显示优势,可以非常清晰的甄别元素之间的关系,明确元素与元素子父DOM的关系树结构,DOM4J作为读写XML文件的小型框架,具有操作简单、性能优越的特点。该系统通过测试已基本实现系统预期设计功能,并且保证功能的高可用性;期间,通过查阅报错信息,及时弥补了存在的缺陷。建议:后期系统更新阶段,可以尝试选择能够处理庞大数据量的MySQL数据库作为存储介质,因为当数据量到达一定的峰值时DOM4J处理XML文档中的数据效率会逐渐降低。本系统设计时,未对XML文档做任何约束,以至于用户录入部分数据可以随心所欲,后期系统更新时可以添加DTD或者Schema相关自定义约束,从而提高系统功能的严谨性。

纯XML语料库语义缓存中辅助翻译工具视图的查找算法研究

详细介绍了纯XML数据库系统的基础知识,包括XML文档缓存结构、基本定义和XML文档的解析方法等。重点分析了序列化XPath查询算法,在分析纯XML数据库语义缓存中辅助翻译工具视图的快速查找算法的优缺点后,给出了一种基于最长视图的补偿查询改进思路。

Modeling Method of Automotive Body CAN/LIN Nets Application Protocol Based on Object-oriented Colored Petri Net

Recently automotive nets are adopted to solve increasing problems in automotive electronic systems.Technologies of automotive local area network from CAN and LIN can solve the problems of the increasing of wire bunch weight and lack in module installation space.However,the multilayer automotive nets software becomes more and more complex,and the development expense is difficult to predict and to keep in check.In this paper,the modeling method of hierarchical automotive nets and the substitution operation based on object-oriented colored Petri net(OOCPN) are proposed.The OOCPN model which analyzes the software structure and validates the collision mechanism of CAN/LIN bus can speed the automobile system development.First,the subsystems are divided and modeled by object-oriented Petri net(OOPN).According to the sets of message sharing relations,the message ports among them are set and the communication gate transitions are defined.Second,the OOPN model is substituted step by step until the inner objects in the automotive body control modules(BCM) are indivisible and colored by colored Petri net(CPN).And the color subsets mark the node messages for the collision mechanism.Third,the OOCPN model of the automotive body CAN/LIN nets is assembled,which keeps the message sets and the system can be expanded.The proposed model is used to analyze features of information sharing among the objects,and it is also used to describe each subsystem real-time behavior of processing messages and implemental device controllers operating,and puts forward a reasonable software framework for the automotive body control subsystem.The research can help to design the communication model in the automotive body system effectively and provide a convenient and rapid way for developing the logical hierarchy software.

NDVI-Based Lacunarity Texture for Improving Identification of Torreya Using Object-Oriented Method

Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, we proposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerning the ＂gappiness＂ or ＂emptiness＂ characteristics. The NDVI-based lacunarity texture was incorporated into object-oriented classification for improving the identification of vegetation categories, especially Torreya which was the targeted tree species in the present research. A three-level hierarchical network of image objects was defined and the proposed texture was integrated as potential sources of information in the rules base. A knowledge base of rules created by classifier C5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the addition of such texture improved the identification of every vegetation category. The results demonstrated that the texture could characterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.

STOCHASTIC OBJECT-ORIENTED PETRI NETS (SOPNS) AND ITS APPLICATION IN MODELING OF MANUFACTURING SYSTEM RELIABILITY

Object-oriented Petri nets (OPNs) is extended into stochastic object-oriented Petri nets (SOPNs) by associating the OPN of an object with stochastic transitions and introducing stochastic places. The stochastic transition of the SOPNs of a production resources can be used to model its reliability, while the SOPN of a production resource can describe its performance with reliability considered. The SOPN model of a case production system is built to illustrate the relationship between the system's performances and the failures of individual production resources.

A Rice Growth Models (RGM) System by Object-Oriented Programming with Visual C++

Object-oriented programming divides the crop production into subsystems and simulates their behaviors. Many classes were designed to simulate the behaviors of different parts or different physiological processes in crop production system. At the same time, many classes have to be employed for bettering user's interface. But how to manage these classes on a higher level to cooperate them into a perfect system is another problem to study. The Rice Growth Models (RGM) system represents an effort to define and implement a framework to manage these classes. In RGM system, the classes were organized into the model-document-view architecture to separate the domain models, data management and user interface. A single document with multiple views interface frame window was adopted in RGM. In the architectures, the simulation models only exchange data with documents while documents act as intermediacies between simulation models and interfaces. Views get data from documents and show the results to users. The classes for the different functions can be grouped into different architectures. Different architectures communicate with each other through documents. The classes for the different functions can be grouped into different architectures. By using the architecture, communication between classes is more efficient. Modeler can add classes in architectures or other architectures to extend the system without having to change system structure, which is useful for construction and maintenance of agricultural system models.

Double Polarization SAR Image Classification based on Object-Oriented Technology

This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.

Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Pine wilt disease(PWD)is currently one of the main causes of large-scale forest destruction.To control the spread of PWD,it is essential to detect affected pine trees quickly.This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD.We used an unmanned aerial vehicle(UAV)platform equipped with an RGB digital camera to obtain high spatial resolution images,and multiscale segmentation was applied to delineate the tree crown,coupling the use of object-oriented classification to classify trees discolored by PWD.Then,the optimal segmentation scale was implemented using the estimation of scale parameter(ESP2)plug-in.The feature space of the segmentation results was optimized,and appropriate features were selected for classification.The results showed that the optimal scale,shape,and compactness values of the tree crown segmentation algorithm were 56,0.5,and 0.8,respectively.The producer’s accuracy(PA),user’s accuracy(UA),and F1 score were 0.722,0.605,and 0.658,respectively.There were no significant classification errors in the final classification results,and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation.The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing.This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.