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.

OBJECT-ORIENTED IMPLEMENTATION OF EXPERT SYSTEMS FOR ENGINE LUBRICATING OIL INSPECTION

The inspection of engine lubricating oil can give an indication of the internal condition of an engine. By means of the Object-Oriented Programming (OOP), an expert system is developed in this paper to computerize the inspection. The traditional components of an expert system, such us knowledge base, inference engine and user interface are reconstructed and integrated, based on the Microsoft Foundation Class (MFC) library. To testify the expert system, an inspection example is given at the end of this paper.

Meta-analysis of potentially confounding effect of class size on associations between object-oriented metrics and maintainability

This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability.To draw as many general conclusions as possible,the confounding effect of class size is analyzed on 127 C＋＋ systems and 113 Java systems.For each OO metric,the indirect effect that represents the distortion of the association caused by class size and its variance for individual systems is first computed.Then,a statistical meta-analysis technique is used to compute the average indirect effect over all the systems and to determine if it is significantly different from zero.The experimental results show that the confounding effects of class size on the associations between OO metrics and maintainability generally exist,regardless of whatever size metric is used.Therefore,empirical studies validating OO metrics on maintainability should consider class size as a confounding variable.

REALIZATION OF OBJECT-ORIENTED RULE-TYPE EXPERT SYSTEM TEMPLATE

Expert systems (ESs) are being increasingly applied to the fault diagnosis of engines. Based on the idea of ES template (EST), an object-oriented rule-type EST is emphatically studied on such aspects as the object-oriented knowledge representation, the heuristic inference engine with an improved depth-first search (DFS) and the graphical user interface. A diagnositic ES instance for debris on magnetic chip detectors (MCDs) is then created with the EST. The spot running shows that the rule-type EST enhances the abilities of knowledge representation and heuristic inference, and breaks a new way for the rapid construction and implementation of ES.

Visual object-oriented application for lane following on intelligent highway system

A visual object-oriented software for lane following on intelligent highway system (IHS) is proposed. According to object-oriented theory, 3 typical user services of self-check, transfer of human driving and automatic running and abnormal information input from the sensors are chosen out. In addition, the functions of real-time display, information exchanging interface, determination and operation interweaving in the 3 user services are separated into 5 object-oriented classes. Moreover, the 5 classes are organized in the visual development environment. At last, experimental result proves the validity and reliability of the control application.

The Design and Implementation of an Object-Oriented Hypermedia System

Presents an object-oriented NBO(node-block-object)data model for hypermedia system.It takes advantage of object-oriented method,encapsulates all multimedia information as well as link functions in one unit,It has successfully achieved cross link to offer much better flexibility and two-way link to realize forward and backward searching in hypermedia system navigation.A conditional relation on links has also been realized,that is very helpful for time sensitive multimedia information processing and multimedia object cooperation.

基于声学FEM-BEM的户内变电站噪声场计算

准确计算户内变电站大型、复杂的噪声场分布,进而评价可采用降噪措施的减噪效果,是解决户内变电站噪声污染的关键问题。为此,综合声学有限元法(finite element method,FEM)求解复杂声场收敛性好及精度高的优点,及声学边界元法(boundary element method,BEM)降维求解大型声场的优势,提出了一种基于声学FEM-BEM的户内变电站噪声场求解算法。首先,建立变电站内部声源声固耦合模型,采用声学FEM求解混响噪声作用下的声固耦合响应;然后,基于声学FEM-BEM耦合理论,求解内、外耦合边界处结构单元受声固耦合激励产生的位移及应力载荷;最后,根据声压及应力载荷激发的外场声波扩散模型,基于常规Gauss数值积分法,建立外部空间声域2维BEM声学积分方程,求解外部声场。该算法在湖南某110 kV户内变电站噪声场的求解分析中得到了成功应用,与实测值的相对误差为3.61%~4.87%。

Shrek:a dynamic object-oriented programming language

From a perspective of theoretical study, there are some faults in the models of the existing object-oriented programming languages. For example, C# does not support metaclasses, the primitive types of Java and C# are not objects, etc. So, this paper designs a programming language, Shrek, which integrates many language features and constructions in a compact and consistent model. The Shrek language is a class-based purely object-oriented language. It has a dynamical strong type system, and adopts a single-inheritance mechanism with Mixin as its complement. It has a consistent class instantiation and inheritance structure, and the ability of intercessive structural computational reflection, which enables it to support safe metaclass programming. It also supports multi-thread programming and automatic garbage collection, and enforces its expressive power by adopting a native method mechanism. The prototype system of the Shrek language is implemented and anticipated design goals are achieved.

基于DEM-FEM耦合方法的碎屑流对桥墩最大冲击力的影响因素研究

碎屑流是我国山区最危险的地质灾害之一,山区桥墩常受到碎屑流冲击而开裂、倾斜甚至倒塌,给山区桥梁建设、运营带来严重的安全隐患。采用离散元方法(discrete element method,DEM)和有限元方法(finite element method,FEM)耦合的三维数值模拟方法模拟了碎屑流对双柱式桥墩的冲击效应,并结合斜槽试验,验证了耦合方法的准确性,进一步分析了碎屑流冲击坡度、距离和体积密度对桥墩冲击力的影响规律。结果表明,最大冲击力与碎屑流冲击坡度、距离和体积密度分别呈幂函数(指数大于1)、幂函数(指数小于1)和线性正相关。冲击坡度、距离和体积密度对最大冲击力的敏感度值分别为3.012、0.202、0.804,在桥梁碎屑流灾害防治时需重视冲击坡度和体积密度的影响。将冲击力的数值模拟值与流体动力学模型预测值对比分析表明,流体动力学模型理论公式能较好地预测桥墩所受的最大冲击力,最大预测误差低于23.6%。相关研究结果可为山区桥梁碎屑流灾害防治与设计提供一定的参考依据。

Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

考虑强度速率衰减效应的地震滑坡SPH-FEM模拟

基于SPH和FEM耦合的数值计算方法,引入滑动面摩擦强度的速率衰减模型,提出了一种能够模拟地震诱发滑坡破坏过程的数值模拟方法。基于所提数值方法模拟了唐家山地震滑坡,模拟结果与现场勘查结果及室内试验现象较为一致。基于模拟的滑动面上摩擦系数的演化过程,将唐家山滑坡的发生分为4个阶段:启动阶段、摩擦衰减阶段、低摩擦滑移阶段和逐步稳定阶段。模拟结果表明速度增加和摩擦强度衰减的相互促进,是触发滑体的高速运动的根本原因。提出采用滑体上作用的动摩擦力fd和动下滑力Td的比值R作为判别指标用于判断大型滑坡的启动,当首次出现R小于1时认为滑动面发生整体贯通并出现失稳启动。基于滑动面不同位置摩擦系数的演化,揭示了滑坡启动中滑动面摩擦强度衰减和滑动面的渐进贯通过程,解释了地震作用与滑动面摩擦参数速率衰减效应共同作用触发大型滑坡发生破坏的内在机理。

Analysis and Design of Soils and Terrain Digital Database (SOTER) Management System Based on Object-Oriented Method

A SOTER management system was developed by analyzing, designing, programming, testing, repeated proceeding and progressing based on the object-oriented method. The function of the attribute database management is inherited and expanded in the new system. The integrity and security of the SOTER database are enhanced. The attribute database management, the spatial database management and the model base are integrated into SOTER based on the component object model (COM), and the graphical user interface (GUI) for Windows is used to interact with clients, thus being easy to create and maintain the SOTER, and convenient to promote the quantification and automation of soil information application.

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.

Object-oriented Urban Dynamic Monitoring——A Case Study of Haidian District of Beijing

It is crucial to conduct the land use/cover research to obtain the global change information.Urban area is one of the most sensitive areas in land use/cover change.Therefore land use/cover change in urban areas is very im-portant in global change.It is vital to incorporate the information of urban land use/cover change into the process of decision-making about urban area development.In this paper,a new urban change detection approach,urban dynamic monitoring based on objects,is introduced.This approach includes four steps:1)producing multi-scale objects from multi-temporal remotely sensed images with spectrum,texture and context information;2)extracting possible changed objects adopting object-oriented classification;3)obtaining shared objects as the basic units for urban change detection;4)determining the threshold to segment the changed objects from the possible changed objects using Otsu method.In this paper,the object-based approach was applied to detecting the urban expansion in Haidian District,Beijing,China with two Landsat Thematic Mapper(TM)data in 1997 and 2004.The results indicated that the overall accuracy was about 84.83%,and Kappa about 0.785.Compared with other conventional approaches,the object-based approach was advantageous in reducing the error accumulation of image classification of each datum and in independence to the radiometric correction and image registration accuracy.

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.

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.

饱和地基水泥土复合桩近场主动隔振的BEM-FEM耦合分析

人工振动污染是当前城市发展过程中面临的一个难题,单排或多排桩是人工振动污染防治的一种重要方式。传统基桩施工容易造成泥浆污染、振动等各种负面环境问题,而水泥土复合桩是一种低振动、低噪声、无挤土、少排泥浆的新型基桩,特别适合在城区构建排桩屏障。针对饱和地基中单排水泥土复合桩的近场主动隔振问题,建立了动力机器基础环境振动影响的半解析BEM方程;在此基础上,分别采用半解析BEM对饱和地基、FEM对水泥土复合桩进行建模,根据饱和地基-水泥土桩接触面的平衡和相容条件,建立了水泥土复合桩近场主动隔振的半解析BEM-FEM耦合方程,给出了耦合方程稀疏矩阵的存储策略,并对单排水泥土复合桩的近场主动隔振效果进行了计算分析。研究结果表明:单排水泥土复合桩能够有效地对动力机器基础引起环境振动进行隔振;等长芯桩复合桩的隔振效果要优于短芯桩。单排水泥土复合桩的隔振效果随着桩数的增加而逐渐提高;较小桩间距并不一定取得更好的隔振效果,建议相邻桩桩间净距取2.0~2.5λ_(R)(Rayleigh波波长)。随着单排水泥土复合桩距振源距离的增加,屏障隔振效果逐渐降低,但降低幅度相对较小。此外,对水泥土复合桩而言,当内插预制桩的外轮廓尺寸相同时,预制芯桩型式对水泥土复合桩隔振效果影响相对较小,可根据实际工程条件选择合适的芯桩类型。

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.