• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.407-416
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• 2009

To support an efficient management of software verification and validation activities, much research has been conducted to predict defects in early phase. And defect prediction models have been proposed to predict defects. But the generality of the models has not been experimentally studied for other software system. In other words, most of prediction models were applied only to the same system that had been used to build the prediction models themselves. Therefore, we performed an experiment to explore generality of major prediction models. In the experiment, we applied three defects prediction models to three different systems. As a result, we cannot find their generality of defect prediction capability. The cause is analyzed to result from a different metric distribution between the systems.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.10-17
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• 2014

A 3D model based on the finite element method (FEM) was built to simulate the infrared thermography (IRT) inspection process. Thermal contrast is an important parameter in IRT and was proven to be a function of defect parameters. Parametric studies were conducted on internal defects with different depths, thicknesses, and orientations. Thermal contrast evolution profiles with respect to the time of the defect and host material were obtained through numerical simulation. The thermal contrast decreased with defect depth and slightly increased with defect thickness. Different orientations of thin defects were detected with IRT, but doing so for thick defects was difficult. These thermal contrast variations with the defect depth, thickness, and orientation can help in optimizing the experimental process and interpretation of data from IRT.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.713-721
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• 2003

The time to market and productivity of embedded system needs a quality measurement process management of embedded software. But, defect management without preemptive analysis or prediction is not useful for quality measurement process management. This subject is focused on the defect that is one of the most important attributes of software measure in the process. Defining of defect attribute and quality measurement process management is according to understanding of embedded sw characteristics and defect data. So, this study contributes to propose the good method of the quantitative based on defect management in the test phase of sw lifecycle.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.48-49
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• 2019

The number of disputes over defects after completion of construction work in apartment buildings is increasing every year. In this situation, the prediction of reasonable defect repair costs is very important. In this paper, we are going to collect basic data for predicting defect repair costs through the correlation analysis of the construction period and defect repair cost of apartment houses. For this purpose, first of all, the construction period and defect repair cost of apartment houses were analyzed to analyze the construction period for each type of work, the construction period for each project type, and the construction period for each standard calculation. Next, the correlation between defect repair cost and the independent variables of the candidate was conducted. According to the analysis, the ratio of framing air, the ratio of finishing air, and the number of delay days showed strong correlation.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.8
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• pp.1213-1219
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• 2001

In this paper, modeling of the multi-pass roll forming process with the finite element method and defect prediction in roll forming process are presented. In the roll forming process, there occurs the defect of scratch. It appears on tubes because of the friction between the strip and the roll, the unexpected sliding velocity and the contact pressure when fabricating the tubes. The surface of the product will be not uniform due to the defect. The scratch can be predicted with the simulation modeling of the finite element method, and can be avoided by modifying the design.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.6
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• pp.834-840
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• 2008

We carried out fatigue testing with materials of aluminum alloyC7075-T6, 2024-T4) by rotary bending fatigue tester. We investigated fatigue limit, fatigue crack initiation, fatigue crack propagation behavior and possibility of fatigue life prediction to the different small circular hole defect. The summarized result are as follows; Fatigue limit of the smooth specimens were related tensile strength and yield strength. In case of more large applied stress and small circular hole crack defect, the fatigue crack was grown rapidly. The fatigue crack propagation behavior proceed at according to inclusion. Fatigue crack propagation ratio appeared instability and retardation phenomenon in the first half of fatigue life but appeared stability and replied in the latter half. On other hand, this experimental data of the materials are appeared fatigue life predictability.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.681-686
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• 2016

Software defect prediction is an important factor in efficient project management and success. The severity of the defect usually determines the degree to which the project is affected. However, existing studies focus only on the presence or absence of a defect and not the severity of defect. In this study, we proposed an ensemble model using FCM based on defect severity. The severity of the defect of NASA data set's PC4 was reclassified. To select the input column that affected the severity of the defect, we extracted the important defect factor of the data set using Random Forest (RF). We evaluated the performance of the model by changing the parameters in the 10-fold cross-validation. The evaluation results were as follows. First, defect severities were reclassified from 58, 40, 80 to 30, 20, 128. Second, BRANCH_COUNT was an important input column for the degree of severity in terms of accuracy and node impurities. Third, smaller tree number led to more variables for good performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.245-265
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• 2022

In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

• Transactions of Materials Processing
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• v.14 no.3 s.75
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• pp.224-232
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• 2005

The high-pressure die-casting is one of the most effective methods to produce a large amount of products in short cycle time. This process, however, has a problem that the gas porosity defect appears easily. The generation of gas porosity is known mainly due to the air entrapment during the injection stage. Most of numerical simulations for the molten metal flow pattern observations have done in the treating of one phase fluid flow but the gas-liquid interface is essentially multi- phase phenomenon. In this paper, the two-phase fluid flow numerical simulation methods have been adapted to predict the gas porosity generations in the molten metal. The accuracy and the usefulness of the new simulation module have been emphasized and verified through some comparison experiments.