• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.2
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• pp.125-135
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• 2004

This paper describes the calculation technique for guided wave propagation with a semi-analytical finite element method (SAFEM) and shows some results of numerical calculation and guided wave simulation for plates, pipes and railway rails. The SAFEM calculation gives dispersion curves and wave structures for bar-like structures. Dispersion curve software for a pipe is introduced, and also dispersion corves for a rail are given and experimentally verified. The mode conversions in a plate with a defect and in a pipe with an elbow or a defect are shown as examples of our guided wave simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.817-828
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• 2018

Modern software, especially web-based software, is broadly used in various fields. Most web applications employ design patterns, such as a model-view-controller (MVC) pattern and a factory pattern as development technology, so the application can have a good architecture to facilitate maintenance and productivity. A web application, however, may have defects and developers must fix the defects when a user submits bug reports. In this paper, we propose a novel approach to improving fault traceability in web application by using software revision information and software behavior model to reduce costs and effectively handle the software defect. We also provide a case study to show effectiveness of our approach.

• Journal of KIISE
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• v.43 no.12
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• pp.1334-1341
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• 2016

In recent years, the importance of managing software defects in the implementation stage has emerged because of the rapid development and wide-range usage of intelligent smart devices. Even if not a few studies have been conducted on the prediction models for software defects, their outcomes have not been widely shared. This paper proposes an efficient probabilistic management model of software metrics based on the Bayesian network, to overcome limits such as binary defect prediction models. We expect the proposed model to configure the Bayesian network by taking advantage of various software metrics, which can help in identifying improvements for refactoring. Once the source code has improved through code refactoring, the measured related metric values will also change. The proposed model presents probability values reflecting the effects after defect removal, which can be achieved by improving metrics through refactoring. This model could cope with the conclusive binary predictions, and consequently secure flexibilities on decision making, using indeterminate probability values.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.73-81
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• 2015

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.11-17
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• 2021

In this study, problems of software completeness appraisal are pointed out and their solutions are presented by analyzing appraisal cases and judicial precedents. Completeness appraisal, ready-made appraisal, defect appraisal, and cost appraisal have been classified as and have been evaluated with extant software completeness appraisals. From a legal point of view, and in judicial precedents, however, there is a big difference between the definition of completeness and the completion rate. This is because the degree of completeness is evaluated under the premise that the software's development is complete, whereas the ready-made appraisal inspects the development progress of unfinished software. Often, in cases involving software completion rate, the total completion level is calculated by weighting each step of the software development process. However, completeness evaluations use the software's realization-operation as its sole criterion. In addition, another issue not addressed in existing software completeness appraisal cases is that there is no mention of who is responsible for software defects, whereas in case law, the responsible party is determined by finding who caused the dispute. In this paper, we systematically classify these problems, and present a novel evaluation method that separates software completeness evaluations from software completion evaluations.

• Current Photovoltaic Research
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• v.8 no.1
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• pp.17-26
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• 2020

It is clear that monocrystalline Silicon (Si) ingots are the key raw material for semiconductors devices. In the present industries markets, most of monocrystalline Silicon (Si) ingots are made by Czochralski Process due to their advantages with low production cost and the big crystal diameters in comparison with other manufacturing process such as Float-Zone technique. However, the disadvantage of Czochralski Process is the presence of impurities such as oxygen or carbon from the quartz and graphite crucible which later will resulted in defects and then lowering the efficiency of Si wafer. The heat transfer plays an important role in the formation of Si ingots. However, the heat transfer generates convection in Si molten state which induces the defects in Si crystal. In this study, a crystal growth simulation software was used to optimize the Si crystal growth process. The furnace and system design were modified. The results showed the melt-crystal interface shape can affect the Si crystal growth rate and defect points. In this study, the defect points and desired interface shape were controlled by specific crystal growth rate condition.

• Applied Microscopy
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• v.39 no.4
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• pp.349-354
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• 2009

In this paper we have constructed three dimensional images and examined structural failure on thin film transistor (TFT) liquid crystal display (LCD) by using dual-beam focused ion beam (FIB) and IMOD software. Specimen was sectioned with dual-beam focused ion beam. Series of two dimensional images were obtained by scanning electron microscopy. Three dimensional reconstruction was constructed from them by using IMOD software. The short defect between Gate layer and Data layer was found from the result of three dimensional reconstruction. That phenomena made the function of the gate lost and data signal supplied to the electrode though the Drain continuously. That signal made continuous line defect. The result of the three dimensional reconstruction, serial section, SEM imaging by using the FIB will be the foundation of the next advanced study.

• Information Systems Review
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• v.14 no.2
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• pp.103-115
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• 2012

Improvement of the capability to cope with risk based on prior preventive management is very important for efficient operation of financial information system. In order to do this, understanding, analysis, and countermeasures for the risk that happened already in the past is essential. In this study, the defect data which happened in the financial information system including account system, business system and data feeding system during 4 years and 5 months were categorized and analyzed by the domain, defect factors, period, day of the week, phases of software development, and defect cause. As a result, it was identified that the defect data had characteristics and trends along the phase of software development, day of the week, and the cause, also that building risk prediction model was necessary for the risk management of whole financial domain due to the relation of the information systems.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.101-109
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• 2012

To develop high quality software, quality control plan is required about fault correction that is latent within software. We should describe fault correction profile properly for this. The tank and pipe model performs complex processes to calculate fault that is remove and escapes. Also, we have to know in which phase the faults were inserted, removed and escaped and know the fault detection rate at any phases. To simplify such complex process, this paper presented model to fault filtering concept. Presented model has advantage that can describe fault more shortly because need not to consider whether was involved in fault that escaped fault is inserted at any step at free step. Also, presented effort estimating model that do fetters in function of fault removal quality and productivity measure and is required in fault detection.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.