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.

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.

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.

UML顺序图的鲁棒性分析

鲁棒性分析技术可以帮助开发人员精确地弥合分析与设计之间的鸿沟。统一建模语言(unified modeling language,UML)顺序图模型被广泛用于面向对象软件系统的分析与设计,它的鲁棒性至关重要。首先,引入形式化模型——统一结构。其次,给出了基于统一结构的描述顺序图的方法和鲁棒性的形式定义,随后讨论顺序图在组合与精化条件下的保存问题。最后,在原型工具支持下进行实例研究。实验结果表明,设计阶段的顺序图能保持分析阶段顺序图的鲁棒性,从而可以给予从事面向对象的开发人员相关帮助及支持。

基于UMCS树的UML类图的混合相似性度量

软件重用是基于给定条件从存储库中检索以前开发的软件产品,检索基于相似性度量。UML(Unified Modeling Language)类图被广泛应用于软件设计,UML类图重用作为软件设计重用的核心而备受关注。因此,对UML类图的相似性开展研究。类图包含语义和结构信息。目前,UML类图的相似性研究主要集中在语义,也有个别讨论结构相似性,但没有考虑将语义和结构相结合。因此,提出一种结合语义和结构的混合相似性度量。鉴于UML类图的非形式化特征,将UML类图转换成图模型,搜索最大公共子图列表,构建了最大公共子图树,提出一个基于最大公共子图序列的混合相似性度量方法。针对概念公共子图和结构公共子图分别定义了语义匹配和结构匹配,并开展了相似性对比和基于相似性的分类质量比较实验,实验结果验证了所提出方法的优势。

基于UML和ASP的艺术类考试成绩查询系统设计与实现

本文采用统一建模语言UML对考试成绩查询系统进行建模,设计了基于UML的艺术类考试成绩查询系统模型,利用ASP技术实现考试成绩查询系统,大大提高艺术类考试成绩管理工作的效率和质量。

基于UML的铁路信号图纸校核系统研究

针对铁路信号系统存在的设备复杂度高、管理维护难等缺点,利用UML技术,依托现有的纸质版CAD图纸,将多元化的图纸进行数据化,通过车站数据库,构建铁路信号图纸校核系统,实现信号图纸的反向验证。介绍铁路信号图纸数据信息化的内容及原理;对铁路信号图纸数据库进行介绍;从系统结构、测试范围、功能验证等方面详细介绍图纸校核系统。该图纸校核系统可以在工程实施中提供施工图审查、校核,提升现场作业效率。

Automatic Generation of UML Class Diagrams for Object-oriented Design and Programing Course

UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.

UML类图的一致性分析

在面向对象开发过程中,UML类图作为表达系统静态结构的最主要方式,蕴含着大量结构信息,使得软件开发阶段各种层次类图之间的一致性难以得到保证。为保证UML类图的一致性,通常将形式化方法与UML类图结合起来对类图精化过程进行分析验证。然而在形式化过程中往往存在UML类图模型信息丢失或增加的问题。引入形式化模型统一结构不会丢失或增加信息,为此首先将UML类图描述为统一结构,给出精化函数的定义;其次给出验证精化函数有效性的算法;最后在开发的原型工具上进行实例验证。对精化过程的验证实验结果表明,该方法可以帮助设计人员及时发现并处理类图中存在的不一致现象。

带有并发行为的UML状态机图的形式语义

在软件开发过程中,UML(统一建模语言)状态机图是目前最流行的建模形式之一,它属于半形式化模型,无法用形式化的方法进行推理。为了能对UML状态机图进行推理,现有工作采用Petri网、时序逻辑语言XYZ/E、动态描述逻辑、Z(Object-Z)语言、CHAM化学抽象机等作为状态机图的形式语义,但这些语义都是行为语义,并没有从结构上直接形成体现真并发的形式语义。该文提出一种新的模型——统一结构模型作为带有并发行为的UML状态机图的形式语义,该模型不会增加或减少状态机图的任何信息。基于统一结构模型首先定义了状态机图的格局(全局状态),用于表现状态机图的执行过程,并且给出了UML状态机图的格局的转换规则,说明格局如何在状态机图中执行,在此基础上给出了状态机图的可达性算法,然后还对状态机图的死锁等性质进行了介绍,最后开发出一个原型工具,实现了状态机图的可达性分析,并用实例说明了该方法的应用。

UML在对象软件工程教学中的应用

作为软件专业的核心专业课程,“软件工程”课程的重要性不言而,其将理论与实践融为一体。当前,面向对象的方式是主要的软件开发方式,因此,结合市场需求特点和技术发展形势,在实际教学中也主要使用此方式。为更好地帮助学生理解专业基础理论知识,以及理解知识的应用方法,并能够将其转化为解决实际问题的实践能力,文章采用UML建模技术进行教学,以提升学生专业素养。

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.

Integrating object-oriented methods and formal methods for requirement engineering

High quality software requirement specification is crucial for a software development. Although much efforts and research works have been done to address the problem, the errors in user requirement are still prevent us from developing high quality software. To address the problem, this paper proposes integrating graphical specification technique UML with formal specification technique to construct user requirement specification. We also present a prototype tool to perform the automatic translation from UML specification into Object-Z specification.

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.

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.

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.