• Proceedings of the IEEK Conference
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• 2002.07b
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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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• v.8 no.2
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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.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.596-600
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.186-200
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• 2006

Analysis model focuses only on functional requirements and postpones nonfunctional requirements and implementation specific issues until subsequent design activities are undertaken. Based on the analysis models, the design activities are performed by refining and clarifying the analysis models. Thus, the quality of analysis models has a vast impact on the design models. Therefore, much effort should be taken to build correct analysis model. In this paper, we propose a set of structural constraints that analysis models of typical object-oriented development methods should satisfy. Three kinds of constraints are proposed: class related constraints, relation related constraints, and usage related constraints. For each constraint, formal definition and description with OCL are provided. In addition, through a case study with two medium-sired industrial systems, we demonstrated that the proposed approach can help to identify and correct serious deficiencies in object-oriented analysis models.

• Safety and Health at Work
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• v.5 no.2
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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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• v.43 no.1
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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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• v.14 no.3
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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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• v.13 no.4
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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.