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.

AN OBJECT-ORIENTED EXPERT SYSTEM FOR TROUBLESHOOTING REFINERY DISTILLATION COLUMNS

An object-oriented prototype expert system ORDEES for off-line trouble-shooting of refinery distillation columns is developed. It is found that highly modular knowledge base can be designed, and different types of data (e.g., graphs, numberical data, and algorithms) may be manipulated, by using object-oriented knowledge representation. In addition, a method termed Object-Oriented Multifunction Switcher is proposed for building multifunction expert systems. The results of the study are expected to be useful for designing multifunction expert systems for complex petroleum refining and petro-chemical processes with many kinds of equipment.

Development of E-Mart Based on Object-Oriented Databases (OODB)

Focusing on the development of electronic-mart (e-mart) based on object-oriented databases (OODB), the concepts of integrated electronic-commerce (e-commerce) environment and e-mart are introduced, and the basic characteristics of OODB Jasmine are described. In addition, the database mode and hierarchy of e-mart are discussed in detail.

Object-oriented software tools for parallel PDE solvers

An object-oriented approach is taken to the problem of formulating portable, easy-to-modify PDE solvers for realistic problems in three space dimensions. The resulting software library, Cogito, contains tools for writing programs to be executed on MIMD computers with distributed memory. Difference methods on composite, structured grids are supported. Most of the Cogito classes have been implemented in Fortran 77, in such a way that the object-oriented design is visible. With respect to parallel performance, these tools yield code that is comparable to parallel solvers written in plain Fortran 77. The resulting programs are can be executed without modification on a large number of multicomputer platforms, and also on serial computers. The uppermost level of abstraction in Cogito concerns the problem of decoupling the numerical method from the PDE problem. The validity of these tools has been preliminarily demonstrated with a C++ implementation for one-dimensional problems.

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.

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.

Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning

Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.

Artificial Intelligence Embedded Object-Oriented Methodology For Model Based

The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section.

Object-oriented crop classification based on UAV remote sensing imagery

UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.

Finite Element Structural Analysis UsingObject-Oriented Techniques

This paper describes the object-oriented implementational method of finite element structural analysis, gives the basic concepts of the object-oriented method and objectoriented programming, develops a complete class hierarchy structure of object-oriented finite element structural analysis, and gives a part C+ + code description.

Object-oriented GIS technology and mine geo-dynamic division information management

Object-oriented methodology was expected to revolutionize GIS design and development, making it easier and faster to design, through a common approach to conceptual modelling, database design, programming languages and user interface tools. In addition, an object-oriented model provides some useful tools for data abstraction and data structuring, which augment the conventional tools and overcome some deficiencies inherent to the traditional relational model. The principles of object-oriented modelling applied to geographic data were put forward, and the concepts of object-oriented paradigm and characteristics of spatial data management were proposed in order to argument their compatibility.

Implementation of Heat Treatment Process Simulation with Object-Oriented Method

In this paper, mathematical models and FEA formulation for implementing heat treatment process simulation were given out. The various coupling effects were treated. The object-oriented methodology of developing heat treatment simulation was explored. The framework of simulating programs was outlined. The main C++ classes were developed, some important member functions were implemented. The present research work shows that using object-oriented method can greatly reduce the amount of coding. The programs are clear in conception, easy to test, modify and expand. By using the methodology introduced in this paper, one heat treatment process three dimensional simulation tool was developed.