• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.920-923
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• 2002

In this paper, we propose Class Diagram With Constraints (CDWC) as an object oriented modeling technique which makes validation possible in software development. CDWC is a simple and basic model for the object oriented analysis, and has a reasonable strictness for software developers. CDWC consists of class diagrams and constraints (invariant and pre/post conditions), using UML and a subset of OCL.. We introduce a method of validation of CDWC using the verification technique of algebraic formal specification language CafeOBJ.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권8호
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• pp.596-600
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• 2009

검증과 확인을 통한 소프트웨어의 효율적인 관리를 지원하기 위하여 객체지향 메트릭 기반의 결함 예측 모형이 많이 제안되고 있다. 제안된 모형은 주로 로지스틱 회귀분석으로 개발하였다. 그리고 개발된 모형의 결함 예측 정확도는 60${\sim}$70%이었다. 본 논문에서는 기존 결함 예측 모형의 효과를 확인하기 위하여 이클립스 3.3을 대상으로 개발된 모형과 유사한 방법으로 실험을 하였다. 실험 결과 모형의 정확성은 약 40%이었다. 이는 주장된 예측력보다 많이 낮은 수치이었다. 또한 단순 로지스틱 회귀분석이 다중 로지스틱 회귀분석보다 높은 예측력을 보였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권2호
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• pp.186-200
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• 2006

분석 모델은 오직 시스템의 기능적 요구사항에 초점을 두며, 비 기능적인 요구사항과 구현과 관련된 이슈들은 이후의 설계 작업이 착수될 때까지 미루어진다. 설계 활동은 분석 모델을 상세화하고 명확하게 하는 방식으로 수행된다. 따라서, 분석 모델의 품질은 설계 모델에 지대한 영향을 미친다. 그러므로, 정확한 분석 모델을 구축하기 위하여 많은 노력을 기울여야 한다. 본 논문에서는 전형적인 객체지향 개발 방법론의 분석 모델이 항상 충족해야 하는 구조적 제약 사항을 제안한다. 제약 사항은 개별 클래스에 관한 제약사항, 클래스간의 관계에 대한 제약 사항과 클래스의 사용에 대한 제약사항으로 분류된다. 각 제약사항 변로 정형적인 정의와 OCL을 이용한 기술이 제공된다. 또한, 2개의 산업체 프로젝트를 대상으로 수행된 사례 연구를 통하여 제안된 기법이 객체지향 분석 모델에 존재하는 심각한 오류를 발견하고 이를 수정하는 데 도움을 줄 수 있음을 보여 준다.

• Safety and Health at Work
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• 제5권2호
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• pp.97-105
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• 2014

Background: Prolonged standing has been hypothesized as a vital contributor to discomfort and muscle fatigue in the workplace. The objective of this study was to develop a decision support system that could provide systematic analysis and solutions to minimize the discomfort and muscle fatigue associated with prolonged standing. Methods: The integration of object-oriented programming and a Model Oriented Simultaneous Engineering System were used to design the architecture of the decision support system. Results: Validation of the decision support system was carried out in two manufacturing companies. The validation process showed that the decision support system produced reliable results. Conclusion: The decision support system is a reliable advisory tool for providing analysis and solutions to problems related to the discomfort and muscle fatigue associated with prolonged standing. Further testing of the decision support system is suggested before it is used commercially.

• Nuclear Engineering and Technology
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• 제43권1호
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• pp.45-62
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• 2011

The Korean nuclear industry is developing a thermal-hydraulic analysis code for safety analysis of pressurized water reactors (PWRs). The new code is called the Safety and Performance Analysis Code for Nuclear Power Plants (SPACE). The SPACE code adopts advanced physical modeling of two-phase flows, mainly two-fluid three-field models which comprise gas, continuous liquid, and droplet fields and has the capability to simulate 3D effects by the use of structured and/or nonstructured meshes. The programming language for the SPACE code is C++ for object-oriented code architecture. The SPACE code will replace outdated vendor supplied codes and will be used for the safety analysis of operating PWRs and the design of advanced reactors. This paper describes the overall features of the SPACE code and shows the code assessment results for several conceptual and separate effect test problems.

• Journal of Information Processing Systems
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• 제14권3호
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.